Tootfinder

Der neueren Filmspielverfilmungen von "Fünf Freunde" sind nur noch bis 26.05.2024 in der #ARD #Mediathek
https://…

This https://arxiv.org/abs/2303.14718 has been replaced.
link: https://scholar.google.com/scholar?q=a

Design and Control of a Small Humanoid Equipped with Flight Unit and Wheels for Multimodal Locomotion
Humanoids are versatile robotic platforms owing to their limbs with multiple degrees of freedom. Although humanoids can walk like humans, they are relatively slow, and cannot run over large barriers. To address these limitations, we aim to achieve rapid terrestrial locomotion ability and simultaneously expand the locomotion domain to the air by utilizing thrust for propulsion. In this paper, we first describe an optimized construction method for a humanoid robot equipped with wheels and a fligh…

do you think linux users are obliged to use the gnulinux keyboard layout

This https://arxiv.org/abs/2405.00015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Experiences Porting Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study
Parallel algorithms relying on synchronous parallelization libraries often experience adverse performance due to global synchronization barriers. Asynchronous many-task runtimes offer task futurization capabilities that minimize or remove the need for global synchronization barriers. This paper conducts a case study of the multidimensional Fast Fourier Transform to identify which applications will benefit from the asynchronous many-task model. Our basis is the popular FFTW library. We use the a…

EY report: the Indian media and entertainment industry grew 8% YoY to $27.9B in 2023; TV shrank 2% YoY to $8.3B due to advertising declining by 6.5% YoY (Naman Ramachandran/Variety)
https://variety.com/2024/film/news/india-media-industry-report-2024…

Indian Media and Entertainment Industry Grows 8% to Reach $28 Billion
The Indian media and entertainment industry grew 8% in 2023 to reach a value of $27.9 billion, according to the annual EY report.

Russian attacks across Ukraine kill 2, injure 27 over past day: https://benborges.xyz/2024/05/06/russian-attacks-across.html

This https://arxiv.org/abs/2403.00111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

A compendium and evaluation of taxonomy quality attributes
Introduction: Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims: Despite this important role of taxonomies in software engineering, their quality is seldom evaluated. Our aim is to identify and define taxonomy quality …

This https://arxiv.org/abs/2302.14595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MateRobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model M…

This https://arxiv.org/abs/2404.13236 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applic…

EY report: the Indian media and entertainment industry grew 8% YoY to $27.9B in 2023; TV shrank 2% YoY to $8.3B due to advertising declining by 6.5% YoY (Naman Ramachandran/Variety)
https://variety.com/2024/film/news/india-media-industry-report-2024…

Indian Media and Entertainment Industry Grows 8% to Reach $28 Billion
The Indian media and entertainment industry grew 8% in 2023 to reach a value of $27.9 billion, according to the annual EY report.

This https://arxiv.org/abs/2401.06401 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

DevEval: Evaluating Code Generation in Practical Software Projects
How to evaluate Large Language Models (LLMs) in code generation is an open question. Many benchmarks have been proposed but are inconsistent with practical software projects, e.g., unreal program distributions, insufficient dependencies, and small-scale project contexts. Thus, the capabilities of LLMs in practical projects are still unclear. In this paper, we propose a new benchmark named DevEval, aligned with Developers' experiences in practical projects. DevEval is collected through a rigorou…

This https://arxiv.org/abs/2211.13093 has been replaced.
link: https://scholar.google.com/scholar?q=a

Autonomous Marker-less Rapid Aerial Grasping
In a future with autonomous robots, visual and spatial perception is of utmost importance for robotic systems. Particularly for aerial robotics, there are many applications where utilizing visual perception is necessary for any real-world scenarios. Robotic aerial grasping using drones promises fast pick-and-place solutions with a large increase in mobility over other robotic solutions. Utilizing Mask R-CNN scene segmentation (detectron2), we propose a vision-based system for autonomous rapid a…

TikTok moves its Creativity Program out of beta, rebranding it as Creator Rewards Program, which requires videos to be longer than a minute to be monetized (Mia Sato/The Verge)
https://www.theverge.com/2024/3/5/24090583/tiktok-c…

TikTok introduces Creator Rewards for users making longer videos
The Creator Rewards Program requires videos longer than one minute for monetization. TikTok has gradually incentivized longer YouTube-like content.

This https://arxiv.org/abs/2312.05772 has been replaced.
link: https://scholar.google.com/scholar?q=a

A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware
Code generation tools are essential to help developers in the software development process. Existing tools often disconnect with the working context, i.e., the code repository, causing the generated code to be not similar to human developers. In this paper, we propose a novel code generation framework, dubbed A^3-CodGen, to harness information within the code repository to generate code with fewer potential logical errors, code redundancy, and library-induced compatibility issues. We identify t…

This https://arxiv.org/abs/2209.08349 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots
In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for car-like Ackermann-steering robots which suffer from non-convex and non-holonomic kinematics. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a given map and destination while avoiding collisions. Specif…

TikTok moves its Creativity Program out of beta, rebranding it as Creator Rewards Program, which requires videos to be longer than a minute to be monetized (Mia Sato/The Verge)
https://www.theverge.com/2024/3/5/24090583/tiktok-c…

TikTok introduces Creator Rewards for users making longer videos
The Creator Rewards Program requires videos longer than one minute for monetization. TikTok has gradually incentivized longer YouTube-like content.

This https://arxiv.org/abs/2403.02175 has been replaced.
link: https://scholar.google.com/scholar?q=a

LiSTA: Geometric Object-Based Change Detection in Cluttered Environments
We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-static scenario where objects are added, subtracted, or changed in position over weeks or months. Our system combines multi-mission LiDAR SLAM, volumetric differencing, object instance description, and c…

This https://arxiv.org/abs/2202.10985 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation
In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives (i.e., paying money) as a strategy to motivate participants' behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motiv…

Tooling Offline Runtime Verification against Interaction Models : recognizing sliced behaviors using parameterized simulation
Erwan Mahe, Boutheina Bannour, Christophe Gaston, Arnault Lapitre, Pascale Le Gall
https://arxiv.org/abs/2403.03083

Tooling Offline Runtime Verification against Interaction Models : recognizing sliced behaviors using parameterized simulation
Offline runtime verification involves the static analysis of executions of a system against a specification. For distributed systems, it is generally not possible to characterize executions in the form of global traces, given the absence of a global clock. To account for this, we model executions as collections of local traces called multi-traces, with one local trace per group of co-localized actors that share a common clock. Due to the difficulty of synchronizing the start and end of the reco…

A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach
Skylar X. Wei, Lu Gan, Joel W. Burdick
https://arxiv.org/abs/2403.03215

A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach
This work presents a novel data-driven multi-layered planning and control framework for the safe navigation of a class of unmanned ground vehicles (UGVs) in the presence of unknown stationary obstacles and additive modeling uncertainties. The foundation of this framework is a novel robust model predictive planner, designed to generate optimal collision-free trajectories given an occupancy grid map, and a paired ancillary controller, augmented to provide robustness against model uncertainties ex…

Toward Improved Deep Learning-based Vulnerability Detection
Adriana Sejfia, Satyaki Das, Saad Shafiq, Nenad Medvidovi\'c
https://arxiv.org/abs/2403.03024

Toward Improved Deep Learning-based Vulnerability Detection
Deep learning (DL) has been a common thread across several recent techniques for vulnerability detection. The rise of large, publicly available datasets of vulnerabilities has fueled the learning process underpinning these techniques. While these datasets help the DL-based vulnerability detectors, they also constrain these detectors' predictive abilities. Vulnerabilities in these datasets have to be represented in a certain way, e.g., code lines, functions, or program slices within which the vu…

MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting
Fangchen Liu, Kuan Fang, Pieter Abbeel, Sergey Levine
https://arxiv.org/abs/2403.03174

MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting
Open-vocabulary generalization requires robotic systems to perform tasks involving complex and diverse environments and task goals. While the recent advances in vision language models (VLMs) present unprecedented opportunities to solve unseen problems, how to utilize their emergent capabilities to control robots in the physical world remains an open question. In this paper, we present MOKA (Marking Open-vocabulary Keypoint Affordances), an approach that employs VLMs to solve robotic manipulatio…

Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Xiuting Ge, Chunrong Fang, Quanjun Zhang, Daoyuan Wu, Bowen Yu, Qirui Zheng, An Guo, Shangwei Lin, Zhihong Zhao, Yang Liu, Zhenyu Chen
https://arxiv.org/abs/2403.02716

Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limited number of labeled warnings to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have been trained through billions …

Biomechanical Comparison of Human Walking Locomotion on Solid Ground and Sand
Chunchu Zhu, Xunjie Chen, Jingang Yi
https://arxiv.org/abs/2403.03105 https:/…

Biomechanical Comparison of Human Walking Locomotion on Solid Ground and Sand
Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanic comparison of human walking locomotion on solid ground and sand. A novel dataset containing 3-dimensional motion and biomechanical data from 20 able-bodied adults for locomotion on solid ground and sand is collected. We present the data collection methods and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics. A comprehensive analys…

How to Save My Gas Fees: Understanding and Detecting Real-world Gas Issues in Solidity Programs
Mengting He, Shihao Xia, Boqin Qin, Nobuko Yoshida, Tingting Yu, Linhai Song, Yiying Zhang
https://arxiv.org/abs/2403.02661

How to Save My Gas Fees: Understanding and Detecting Real-world Gas Issues in Solidity Programs
The execution of smart contracts on Ethereum, a public blockchain system, incurs a fee called gas fee for its computation and data-store consumption. When programmers develop smart contracts (e.g., in the Solidity programming language), they could unknowingly write code snippets that unnecessarily cause more gas fees. These issues, or what we call gas wastes, could lead to significant monetary waste for users. Yet, there have been no systematic examination of them or effective tools for detecti…

Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space
Qianhao Wang, Zhepei Wang, Chao Xu, Fei Gao
https://arxiv.org/abs/2403.02977

Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space
1) Restrictive Inflation is designed to ensure the managibility of the generated convex polytope. Based on its characteristic of few variables but rich constraints, an efficient and numerically stable solver is designed. 2) A novel method that formulates the MVIE problem into SOCP formulation is proposed, which avoids directly confronting the positive definite constraints and improves the computational efficiency. 3) Especially for 2-D MVIE, a linear-time exact algorithm is introduced for the f…

AlloyInEcore: Embedding of First-Order Relational Logic into Meta-Object Facility for Automated Model Reasoning
Ferhat Erata, Arda Goknil, Ivan Kurtev, Bedir Tekinerdogan
https://arxiv.org/abs/2403.02652

AlloyInEcore: Embedding of First-Order Relational Logic into Meta-Object Facility for Automated Model Reasoning
We present AlloyInEcore, a tool for specifying metamodels with their static semantics to facilitate automated, formal reasoning on models. Software development projects require that software systems be specified in various models (e.g., requirements models, architecture models, test models, and source code). It is crucial to reason about those models to ensure the correct and complete system specifications. AlloyInEcore allows the user to specify metamodels with their static semantics, while, u…

Online Learning of Human Constraints from Feedback in Shared Autonomy
Shibei Zhu, Tran Nguyen Le, Samuel Kaski, Ville Kyrki
https://arxiv.org/abs/2403.02974

Online Learning of Human Constraints from Feedback in Shared Autonomy
Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on learning safety constraints for collaboration, or how to divide and distribute the subtasks between the participating agents to carry out the main task. In contrast, we propose to learn a human constraints model that, in addition, considers the diverse behaviors of different human operators. We consider a type of col…

Generative Software Engineering
Yuan Huang, Yinan Chen, Xiangping Chen, Junqi Chen, Rui Peng, Zhicao Tang, Jinbo Huang, Furen Xu, Zibin Zheng
https://arxiv.org/abs/2403.02583

Generative Software Engineering
The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which gener…

Single-Channel Robot Ego-Speech Filtering during Human-Robot Interaction
Yue Li, Koen V Hindriks, Florian Kunneman
https://arxiv.org/abs/2403.02918 https:/…

Single-Channel Robot Ego-Speech Filtering during Human-Robot Interaction
In this paper, we study how well human speech can automatically be filtered when this overlaps with the voice and fan noise of a social robot, Pepper. We ultimately aim for an HRI scenario where the microphone can remain open when the robot is speaking, enabling a more natural turn-taking scheme where the human can interrupt the robot. To respond appropriately, the robot would need to understand what the interlocutor said in the overlapping part of the speech, which can be accomplished by targe…

[2024-03-06 Wed (UTC), 6 new articles found for cs.SE Software Engineering]

A Miniaturized Device for Ultrafast On-demand Drug Release based on a Gigahertz Ultrasonic Resonator
Yangchao Zhou, Moonkwang Jeong, Meng Zhang, Xuexin Duan, Tian Qiu
https://arxiv.org/abs/2403.02917

A Miniaturized Device for Ultrafast On-demand Drug Release based on a Gigahertz Ultrasonic Resonator
On-demand controlled drug delivery is essential for the treatment of a wide range of chronic diseases. As the drug is released at the time when required, its efficacy is boosted and the side effects are minimized. However, so far, drug delivery devices often rely on the passive diffusion process for a sustained release, which is slow and uncontrollable. Here, we present a miniaturized microfluidic device for wirelessly controlled ultrafast active drug delivery, driven by an oscillating solid-li…

This https://arxiv.org/abs/2401.06401 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

DevEval: Evaluating Code Generation in Practical Software Projects
How to evaluate Large Language Models (LLMs) in code generation is an open question. Many benchmarks have been proposed but are inconsistent with practical software projects, e.g., unreal program distributions, insufficient dependencies, and small-scale project contexts. Thus, the capabilities of LLMs in practical projects are still unclear. In this paper, we propose a new benchmark named DevEval, aligned with Developers' experiences in practical projects. DevEval is collected through a rigorou…

OORD: The Oxford Offroad Radar Dataset
Matthew Gadd, Daniele De Martini, Oliver Bartlett, Paul Murcutt, Matt Towlson, Matthew Widojo, Valentina Mu\c{s}at, Luke Robinson, Efimia Panagiotaki, Georgi Pramatarov, Marc Alexander K\"uhn, Letizia Marchegiani, Paul Newman, Lars Kunze
https://arxiv.org/abs/2403.02845

OORD: The Oxford Offroad Radar Dataset
There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although several datasets to support this research area have been released, they are primarily focused on urban or semi-urban environments. Nevertheless, rugged offroad deployments are important application areas which also present unique challenges and opportunities for this sensor technology. Therefore, the Oxford Offroad Radar …

This https://arxiv.org/abs/2311.07277 has been replaced.
link: https://scholar.google.com/scholar?q=a

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection
Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can…

SpaceHopper: A Small-Scale Legged Robot for Exploring Low-Gravity Celestial Bodies
Alexander Spiridonov, Fabio Buehler, Moriz Berclaz, Valerio Schelbert, Jorit Geurts, Elena Krasnova, Emma Steinke, Jonas Toma, Joschua Wuethrich, Recep Polat, Wim Zimmermann, Philip Arm, Nikita Rudin, Hendrik Kolvenbach, Marco Hutter
https://arxiv…

SpaceHopper: A Small-Scale Legged Robot for Exploring Low-Gravity Celestial Bodies
We present SpaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2kg and has a body size of 245mm while using space-qualifiable components. Furthermore, SpaceHopper's design and controls make it well-adapted for investigating dynamic locomotion modes with extended flight-phases. Instead of gyroscopes or fly-wheels, the system uses its three legs to reorient the body during flight in preparation for landing. We control th…

This https://arxiv.org/abs/2310.12357 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code. This paper seeks to…

LodeStar: Maritime Radar Descriptor for Semi-Direct Radar Odometry
Hyesu Jang, Minwoo Jung, Myung-Hwan Jeon, Ayoung Kim
https://arxiv.org/abs/2403.02773 ht…

LodeStar: Maritime Radar Descriptor for Semi-Direct Radar Odometry
Maritime radars are prevalently adopted to capture the vessel's omnidirectional data as imagery. Nevertheless, inherent challenges persist with marine radars, including limited frequency, suboptimal resolution, and indeterminate detections. Additionally, the scarcity of discernible landmarks in the vast marine expanses remains a challenge, resulting in consecutive scenes that often lack matching feature points. In this context, we introduce a resilient maritime radar scan representation LodeSta…

This https://arxiv.org/abs/2207.10223 has been replaced.
link: https://scholar.google.com/scholar?q=a

Fairness Testing: A Comprehensive Survey and Analysis of Trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, a…

Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
Timothy Chen, Ola Shorinwa, Weijia Zeng, Joseph Bruno, Philip Dames, Mac Schwager
https://arxiv.org/abs/2403.02751

Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
We present Splat-Nav, a navigation pipeline that consists of a real-time safe planning module and a robust state estimation module designed to operate in the Gaussian Splatting (GSplat) environment representation, a popular emerging 3D scene representation from computer vision. We formulate rigorous collision constraints that can be computed quickly to build a guaranteed-safe polytope corridor through the map. We then optimize a B-spline trajectory through this corridor. We also develop a real-…

This https://arxiv.org/abs/2109.01002 has been replaced.
link: https://scholar.google.com/scholar?q=a

DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
Input constraints are useful for many software development tasks. For example, input constraints of a function enable the generation of valid inputs, i.e., inputs that follow these constraints, to test the function deeper. API functions of deep learning (DL) libraries have DL specific input constraints, which are described informally in the free form API documentation. Existing constraint extraction techniques are ineffective for extracting DL specific input constraints. To fill this gap, we …

RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
Priya Sundaresan, Quan Vuong, Jiayuan Gu, Peng Xu, Ted Xiao, Sean Kirmani, Tianhe Yu, Michael Stark, Ajinkya Jain, Karol Hausman, Dorsa Sadigh, Jeannette Bohg, Stefan Schaal
https://arxiv.org/abs/2403.02709

RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like language, but similar to images they can also help a downstream policy to be spatially-aware and even go beyond images to disambiguate task-…

Challenges of Processing Data Clumps within Plugin Architectures of Integrated Development Environment
Nils Baumgartner, Elke Pulverm\"uller
https://arxiv.org/abs/2403.03903 …

Challenges of Processing Data Clumps within Plugin Architectures of Integrated Development Environment
In this study, we explore advanced strategies for enhancing software quality by detecting and refactoring data clumps, special types of code smells. Our approach transcends the capabilities of integrated development environments, utilizing a novel method that separates the detection of data clumps from the source access. This method facilitates data clump processing. We introduce a command-line interface plugin to support this novel method of processing data clumps. This research highlights the…

UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
Ohn Kim, Junwon Seo, Seongyong Ahn, Chong Hui Kim
https://arxiv.org/abs/2403.02642

UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
Autonomous off-road navigation requires an accurate semantic understanding of the environment, often converted into a bird's-eye view (BEV) representation for various downstream tasks. While learning-based methods have shown success in generating local semantic terrain maps directly from sensor data, their efficacy in off-road environments is hindered by challenges in accurately representing uncertain terrain features. This paper presents a learning-based fusion method for generating dense terr…

Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing
Asmita, Yaroslav Oliinyk, Michael Scott, Ryan Tsang, Chongzhou Fang, Houman Homayoun
https://arxiv.org/abs/2403.03897

Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing
BusyBox, an open-source software bundling over 300 essential Linux commands into a single executable, is ubiquitous in Linux-based embedded devices. Vulnerabilities in BusyBox can have far-reaching consequences, affecting a wide array of devices. This research, driven by the extensive use of BusyBox, delved into its analysis. The study revealed the prevalence of older BusyBox versions in real-world embedded products, prompting us to conduct fuzz testing on BusyBox. Fuzzing, a pivotal software t…

TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes
Cui Chao, Zhao Jiankang
https://arxiv.org/abs/2403.02618 https://

TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes
As the errors of microelectromechanical system (MEMS) gyroscopes are complex and nonlinear, the current calibration methods, which rely on linear models or networks with numerous parameters, are inadequate for low-cost embedded computing platforms to achieve both precision and real-time performance. In this paper, we introduce a extremely tiny network (TGC-Net) that characterizes the measurement model of MEMS gyroscopes. The network has a small number of parameters and can be trained on a centr…

Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
Aaron Imani, Shiva Radmanesh, Iftekhar Ahmed, Mohammad Moshirpour
https://arxiv.org/abs/2403.03819

Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
In recent years, open-source software (OSS) has become increasingly prevalent in developing software products. While OSS documentation is the primary source of information provided by the developers' community about a product, its role in the industry's adoption process has yet to be examined. We conducted semi-structured interviews and an online survey to provide insight into this area. Based on interviews and survey insights, we developed a topic model to collect relevant information from OSS…

A Reduced-Order Resistive Force Model for Robotic Foot-Mud Interactions
Xunjie Chen, Jingang Yi, Jerry Shan
https://arxiv.org/abs/2403.02617 https://

A Reduced-Order Resistive Force Model for Robotic Foot-Mud Interactions
Legged robots are well-suited for broad exploration tasks in complex environments with yielding terrain. Understanding robotic foot-terrain interactions is critical for safe locomotion and walking efficiency for legged robots. This paper presents a reduced-order resistive-force model for robotic-foot/mud interactions. We focus on vertical robot locomotion on mud and propose a visco-elasto-plastic analog to model the foot/mud interaction forces. Dynamic behaviors such as mud visco-elasticity, wi…

Trigram-Based Persistent IDE Indices with Quick Startup
Zakhar Iakovlev, Alexey Chulkov, Nikita Golikov, Vyacheslav Lukianov, Nikita Zinoviev, Dmitry Ivanov, Vitaly Aksenov
https://arxiv.org/abs/2403.03751

Trigram-Based Persistent IDE Indices with Quick Startup
One common way to speed up the find operation within a set of text files involves a trigram index. This structure is merely a map from a trigram (sequence consisting of three characters) to a set of files which contain it. When searching for a pattern, potential file locations are identified by intersecting the sets related to the trigrams in the pattern. Then, the search proceeds only in these files. However, in a code repository, the trigram index evolves across different versions. Upon che…

UniDoorManip: Learning Universal Door Manipulation Policy Over Large-scale and Diverse Door Manipulation Environments
Yu Li, Xiaojie Zhang, Ruihai Wu, Zilong Zhang, Yiran Geng, Hao Dong, Zhaofeng He
https://arxiv.org/abs/2403.02604

UniDoorManip: Learning Universal Door Manipulation Policy Over Large-scale and Diverse Door Manipulation Environments
Learning a universal manipulation policy encompassing doors with diverse categories, geometries and mechanisms, is crucial for future embodied agents to effectively work in complex and broad real-world scenarios. Due to the limited datasets and unrealistic simulation environments, previous works fail to achieve good performance across various doors. In this work, we build a novel door manipulation environment reflecting different realistic door manipulation mechanisms, and further equip this en…

The Visual Debugger: Past, Present, and Future
Tim Kr\"auter, Patrick St\"unkel, Adrian Rutle, Yngve Lamo
https://arxiv.org/abs/2403.03683 https:…

The Visual Debugger: Past, Present, and Future
The Visual Debugger is an IntelliJ IDEA plugin that presents debug information as an object diagram to enhance program understanding. Reflecting on our past development, we detail the lessons learned and roadblocks we have experienced while implementing and integrating the Visual Debugger into the IntelliJ IDEA. Furthermore, we describe recent improvements to the Visual Debugger, greatly enhancing the plugin in the present. Looking into the future, we propose solutions to overcome the roadblock…

Purpose for Open-Ended Learning Robots: A Computational Taxonomy, Definition, and Operationalisation
Gianluca Baldassarre, Richard J. Duro, Emilio Cartoni, Mehdi Khamassi, Alejandro Romero, Vieri Giuliano Santucci
https://arxiv.org/abs/2403.02514

Purpose for Open-Ended Learning Robots: A Computational Taxonomy, Definition, and Operationalisation
Autonomous open-ended learning (OEL) robots are able to cumulatively acquire new skills and knowledge through direct interaction with the environment, for example relying on the guidance of intrinsic motivations and self-generated goals. OEL robots have a high relevance for applications as they can use the autonomously acquired knowledge to accomplish tasks relevant for their human users. OEL robots, however, encounter an important limitation: this may lead to the acquisition of knowledge that …

Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
Huy Le, Philipp Schillinger, Miroslav Gabriel, Alexander Qualmann, Ngo Anh Vien
https://arxiv.org/abs/2403.02495

Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios. These scenarios may involve previously unseen objects, variations in camera perspectives, and bin configurations, among other factors. In this paper, we introduce a novel approach, SSL-ConvSAC, that combines semi-supervised learning and reinforcement learning for online grasp learning. By treating pixels with r…

Demonstrating a Robust Walking Algorithm for Underactuated Bipedal Robots in Non-flat, Non-stationary Environments
Oluwami Dosunmu-Ogunbi, Aayushi Shrivastava, Jessy W Grizzle
https://arxiv.org/abs/2403.02486

Demonstrating a Robust Walking Algorithm for Underactuated Bipedal Robots in Non-flat, Non-stationary Environments
This work explores an innovative algorithm designed to enhance the mobility of underactuated bipedal robots across challenging terrains, especially when navigating through spaces with constrained opportunities for foot support, like steps or stairs. By combining ankle torque with a refined angular momentum-based linear inverted pendulum model (ALIP), our method allows variability in the robot's center of mass height. We employ a dual-strategy controller that merges virtual constraints for preci…

Exposure-Conscious Path Planning for Equal-Exposure Corridors
Eugene T. Hamzezadeh, John G. Rogers, Neil T. Dantam, Andrew J. Petruska
https://arxiv.org/abs/2403.02450

Exposure-Conscious Path Planning for Equal-Exposure Corridors
While maximizing line-of-sight coverage of specific regions or agents in the environment is a well explored path planning objective, the converse problem of minimizing exposure to the entire environment during navigation is especially interesting in the context of minimizing detection risk. This work demonstrates that minimizing line-of-sight exposure to the environment is non-Markovian, which cannot be efficiently solved optimally with traditional path planning. The optimality gap of the graph…

Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models
Dimitris Papadimitriou, Daniel S. Brown
https://arxiv.org/abs/2403.02431

Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models
It is crucial for robots to be aware of the presence of constraints in order to acquire safe policies. However, explicitly specifying all constraints in an environment can be a challenging task. State-of-the-art constraint inference algorithms learn constraints from demonstrations, but tend to be computationally expensive and prone to instability issues. In this paper, we propose a novel Bayesian method that infers constraints based on preferences over demonstrations. The main advantages of our…

[2024-03-06 Wed (UTC), 21 new articles found for cs.RO Robotics]

This https://arxiv.org/abs/2403.01890 has been replaced.
link: https://scholar.google.com/scholar?q=a

Aerial Tensile Perching and Disentangling Mechanism for Long-Term Environmental Monitoring
Aerial robots show significant potential for forest canopy research and environmental monitoring by providing data collection capabilities at high spatial and temporal resolutions. However, limited flight endurance hinders their application. Inspired by natural perching behaviours, we propose a multi-modal aerial robot system that integrates tensile perching for energy conservation and a suspended actuated pod for data collection. The system consists of a quadrotor drone, a slewing ring mechani…

This https://arxiv.org/abs/2403.01537 has been replaced.
link: https://scholar.google.com/scholar?q=a

Mixed-Strategy Nash Equilibrium for Crowd Navigation
We address the problem of finding mixed-strategy Nash equilibrium for crowd navigation. Mixed-strategy Nash equilibrium provides a rigorous model for the robot to anticipate uncertain yet cooperative human behavior in crowds, but the computation cost is often too high for scalable and real-time decision-making. Here we prove that a simple iterative Bayesian updating scheme converges to the Nash equilibrium of a mixed-strategy social navigation game. Furthermore, we propose a data-driven framewo…

This https://arxiv.org/abs/2402.18420 has been replaced.
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CafkNet: GNN-Empowered Forward Kinematic Modeling for Cable-Driven Parallel Robots
When deploying Cable-Driven Parallel Robots (CDPRs) in practice, one of the challenges is kinematic modeling. Unlike serial mechanisms, CDPRs have a simple inverse kinematics problem but a complex forward kinematics (FK) issue. Therefore, the development of accurate and efficient FK solvers has been a prominent research focus in CDPR applications. By observing the topology within CDPRs, in this letter, we propose a graph-based representation to model CDPRs and introduce CafkNet, a fast and gene…

This https://arxiv.org/abs/2402.02192 has been replaced.
link: https://scholar.google.com/scholar?q=a

RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction
In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet's methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent…

This https://arxiv.org/abs/2311.00390 has been replaced.
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A Modular Pneumatic Soft Gripper Design for Aerial Grasping and Landing
Aerial robots have garnered significant attention due to their potential applications in various industries, such as inspection, search and rescue, and drone delivery. Successful missions often depend on the ability of these robots to grasp and land effectively. This paper presents a novel modular soft gripper design tailored explicitly for aerial grasping and landing operations. The proposed modular pneumatic soft gripper incorporates a feed-forward proportional controller to regulate pressure…

This https://arxiv.org/abs/2310.10023 has been replaced.
link: https://scholar.google.com/scholar?q=a

3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simul…

This https://arxiv.org/abs/2310.02791 has been replaced.
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R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators
This paper presents an optimization-based solution to task and motion planning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) has shown promising capabilities for optimally dealing with hybrid TAMP problems that involve abstract and geometric constraints. However, LGP does not scale well to high-dimensional systems (e.g. mobile manipulators) and can suffer from obstacle avoidance issues due to local minima. In this work, we extend LGP with a sampling-based reachability graph t…

This https://arxiv.org/abs/2309.16445 has been replaced.
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db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning
This paper presents a multi-robot kinodynamic motion planner that enables a team of robots with different dynamics, actuation limits, and shapes to reach their goals in challenging environments. We solve this problem by combining Conflict-Based Search (CBS), a multi-agent path finding method, and discontinuity-bounded A*, a single-robot kinodynamic motion planner. Our method, db-CBS, operates in three levels. Initially, we compute trajectories for individual robots using a graph search that all…

This https://arxiv.org/abs/2309.15065 has been replaced.
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Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding
Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and Mapping (SLAM) system that harnesses the open-vocabulary nature of Large Language Models (LLMs) to create a unified approach to scene understanding and place recognition. The approach …

This https://arxiv.org/abs/2309.14737 has been replaced.
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Volumetric Semantically Consistent 3D Panoptic Mapping
We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments. The proposed approach is based on a Voxel-TSDF representation used in recent algorithms. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. Further improvements are achieved by graph optimization-based s…

This https://arxiv.org/abs/2309.14225 has been replaced.
link: https://scholar.google.com/scholar?q=a

HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component …

This https://arxiv.org/abs/2309.13948 has been replaced.
link: https://scholar.google.com/scholar?q=a

Co-Design Optimisation of Morphing Topology and Control of Winged Drones
The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone…

This https://arxiv.org/abs/2309.08849 has been replaced.
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Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critic…

This https://arxiv.org/abs/2309.08322 has been replaced.
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Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet
Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discr…

This https://arxiv.org/abs/2307.13991 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation
Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a glob…

This https://arxiv.org/abs/2307.06924 has been replaced.
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This https://arxiv.org/abs/2306.09281 has been replaced.
link: https://scholar.google.com/scholar?q=a

This https://arxiv.org/abs/2304.00346 has been replaced.
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This https://arxiv.org/abs/2301.13413 has been replaced.
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Fine Robotic Manipulation without Force/Torque Sensor
Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using onl…

This https://arxiv.org/abs/2403.03105 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Biomechanical Comparison of Human Walking Locomotion on Solid Ground and Sand
Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanic comparison of human walking locomotion on solid ground and sand. A novel dataset containing 3-dimensional motion and biomechanical data from 20 able-bodied adults for locomotion on solid ground and sand is collected. We present the data collection methods and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics. A comprehensive analys…

This https://arxiv.org/abs/2403.01766 has been replaced.
link: https://scholar.google.com/scholar?q=a

Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction
Social robots often rely on visual perception to understand their users and the environment. Recent advancements in data-driven approaches for computer vision have demonstrated great potentials for applying deep-learning models to enhance a social robot's visual perception. However, the high computational demands of deep-learning methods, as opposed to the more resource-efficient shallow-learning models, bring up important questions regarding their effects on real-world interaction and user exp…

This https://arxiv.org/abs/2402.18759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning with Language-Guided State Abstractions
We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations, which can surface important features of an environment and hide irrelevant ones. These state representations are typically manually specified, or derived from other labor-intensive labeling procedures. Our method, LGA (language-guided abstraction), uses a combination of …

This https://arxiv.org/abs/2402.18394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Dual-IMU State Estimation for Relative Localization of Two Mobile Agents
In this paper, we address the problem of relative localization of two mobile agents. Specifically, we consider the Dual-IMU system, where each agent is equipped with one IMU, and employs relative pose observations between them. Previous works, however, typically assumed known ego motion and ignored biases of the IMUs. Instead, we study the most general case of unknown biases for both IMUs. Besides the derivation of dynamic model equations of the proposed system, we focus on the observability an…

This https://arxiv.org/abs/2402.17933 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ICAT: An Indoor Connected and Autonomous Testbed for Vehicle Computing
Indoor autonomous driving testbeds have emerged to complement expensive outdoor testbeds and virtual simulations, offering scalable and cost-effective solutions for research in navigation, traffic optimization, and swarm intelligence. However, they often lack the robust sensing and computing infrastructure for advanced research. Addressing these limitations, we introduce the Indoor Connected Autonomous Testbed (ICAT), a platform that not only tackles the unique challenges of indoor autonomous d…

This https://arxiv.org/abs/2402.16796 has been replaced.
link: https://scholar.google.com/scholar?q=a

Expressive Whole-Body Control for Humanoid Robots
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a policy, we leverage the large-scale human motion capture data from the graphics community in a Reinforcement Learning framework. However, directly performing imitation learning with the motion capture dataset would not work on the real humanoid robot, given the la…

This https://arxiv.org/abs/2402.10329 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interfa…

This https://arxiv.org/abs/2312.02731 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning
Many real-world sequential manipulation tasks involve a combination of discrete symbolic search and continuous motion planning, collectively known as combined task and motion planning (TAMP). However, prevailing methods often struggle with the computational burden and intricate combinatorial challenges, limiting their applications for online replanning in the real world. To address this, we propose Dynamic Logic-Geometric Program (D-LGP), a novel approach integrating Dynamic Tree Search and glo…

This https://arxiv.org/abs/2311.11016 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SNI-SLAM: Semantic Neural Implicit SLAM
We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, and robust camera tracking. In this system, we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition, to fully utilize the correlation between multiple attributes of the environment, we integrate appearance, geometry…

This https://arxiv.org/abs/2310.10023 has been replaced.
link: https://scholar.google.com/scholar?q=a

3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simul…

This https://arxiv.org/abs/2310.03379 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Safe Reinforcement Learning via Hierarchical Adaptive Chance-Constraint Safeguards
Ensuring safety in Reinforcement Learning (RL), typically framed as a Constrained Markov Decision Process (CMDP), is crucial for real-world exploration applications. Current approaches in handling CMDP struggle to balance optimality and feasibility, as direct optimization methods cannot ensure state-wise in-training safety, and projection-based methods correct actions inefficiently through lengthy iterations. To address these challenges, we propose Adaptive Chance-constrained Safeguards (ACS), …

This https://arxiv.org/abs/2310.01986 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Vision-Based Tactile Sensing System for Multimodal Contact Information Perception via Neural Network
In general, robotic dexterous hands are equipped with various sensors for acquiring multimodal contact information such as position, force, and pose of the grasped object. This multi-sensor-based design adds complexity to the robotic system. In contrast, vision-based tactile sensors employ specialized optical designs to enable the extraction of tactile information across different modalities within a single system. Nonetheless, the decoupling design for different modalities in common systems is…

This https://arxiv.org/abs/2309.16219 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Sensorless Estimation of Contact Using Deep-Learning for Human-Robot Interaction
Physical human-robot interaction has been an area of interest for decades. Collaborative tasks, such as joint compliance, demand high-quality joint torque sensing. While external torque sensors are reliable, they come with the drawbacks of being expensive and vulnerable to impacts. To address these issues, studies have been conducted to estimate external torques using only internal signals, such as joint states and current measurements. However, insufficient attention has been given to friction…

This https://arxiv.org/abs/2309.12543 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Real-time Batched Distance Computation for Time-Optimal Safe Path Tracking
In human-robot collaboration, there has been a trade-off relationship between the speed of collaborative robots and the safety of human workers. In our previous paper, we introduced a time-optimal path tracking algorithm designed to maximize speed while ensuring safety for human workers. This algorithm runs in real-time and provides the safe and fastest control input for every cycle with respect to ISO standards. However, true optimality has not been achieved due to inaccurate distance computat…

This https://arxiv.org/abs/2309.12251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Planning Optimal Trajectories for Mobile Manipulators under End-effector Trajectory Continuity Constraint
Mobile manipulators have been employed in many applications that are traditionally performed by either multiple fixed-base robots or a large robotic system. This capability is enabled by the mobility of the mobile base. However, the mobile base also brings redundancy to the system, which makes mobile manipulator motion planning more challenging. In this paper, we tackle the mobile manipulator motion planning problem under the end-effector trajectory continuity constraint in which the end-effect…

3D Diffusion Policy
Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, Huazhe Xu
https://arxiv.org/abs/2403.03954 https://…

3D Diffusion Policy
Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact…

Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, Pulkit Agrawal
https://arxiv.org/abs/2403.03949

Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robust behaviors but may require impractical amounts of unsafe real-world data collection. To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system …

Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation
Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James
https://arxiv.org/abs/2403.03890

Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation
This paper introduces Hierarchical Diffusion Policy (HDP), a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP), and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-le…

Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
Zifan Xu, Amir Hossain Raj, Xuesu Xiao, Peter Stone
https://arxiv.org/abs/2403.03848

Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
Recent advances of locomotion controllers utilizing deep reinforcement learning (RL) have yielded impressive results in terms of achieving rapid and robust locomotion across challenging terrain, such as rugged rocks, non-rigid ground, and slippery surfaces. However, while these controllers primarily address challenges underneath the robot, relatively little research has investigated legged mobility through confined 3D spaces, such as narrow tunnels or irregular voids, which impose all-around co…

Confidence-Aware Decision-Making and Control for Tool Selection
Ajith Anil Meera, Pablo Lanillos
https://arxiv.org/abs/2403.03808 https://

Confidence-Aware Decision-Making and Control for Tool Selection
Self-reflecting about our performance (e.g., how confident we are) before doing a task is essential for decision making, such as selecting the most suitable tool or choosing the best route to drive. While this form of awareness -- thinking about our performance or metacognitive performance -- is well-known in humans, robots still lack this cognitive ability. This reflective monitoring can enhance their embodied decision power, robustness and safety. Here, we take a step in this direction by int…

A Precision Drone Landing System using Visual and IR Fiducial Markers and a Multi-Payload Camera
Joshua Springer, Gylfi {\TH}\'or Gu{\dh}mundsson, Marcel Kyas
https://arxiv.org/abs/2403.03806

A Precision Drone Landing System using Visual and IR Fiducial Markers and a Multi-Payload Camera
We propose a method for autonomous precision drone landing with fiducial markers and a gimbal-mounted, multi-payload camera with wide-angle, zoom, and IR sensors. The method has minimal data requirements; it depends primarily on the direction from the drone to the landing pad, enabling it to switch dynamically between the camera's different sensors and zoom factors, and minimizing auxiliary sensor requirements. It eliminates the need for data such as altitude above ground level, straight-line d…

Emotional Tandem Robots: How Different Robot Behaviors Affect Human Perception While Controlling a Mobile Robot
Julian Kaduk, Friederike Weilbeer, Heiko Hamann
https://arxiv.org/abs/2403.03746

Emotional Tandem Robots: How Different Robot Behaviors Affect Human Perception While Controlling a Mobile Robot
In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follower scenario and study how emotionally expressive motion patterns of a small, mobile follower robot affect the perception of a human operat…

Robust MITL planning under uncertain navigation times
Alexis Linard, Anna Gautier, Daniel Duberg, Jana Tumova
https://arxiv.org/abs/2403.03727 https://

Robust MITL planning under uncertain navigation times
In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL) - a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a …

3D Object Visibility Prediction in Autonomous Driving
Chuanyu Luo, Nuo Cheng, Ren Zhong, Haipeng Jiang, Wenyu Chen, Aoli Wang, Pu Li
https://arxiv.org/abs/2403.03681

3D Object Visibility Prediction in Autonomous Driving
With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth. The prevailing framework for multi-sensor autonomous driving encompasses sensor installation, perception, path planning, decision-making, and motion control. At the perception phase, a common approach involves utilizing neural networks to infer 3D bounding box (Bbox) attributes from raw sensor data, including classification, size, and orientation. In this paper, we prese…