Tootfinder

SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
Haoyuan Deng, Wenkai Guo, Qianzhun Wang, Zhenyu Wu, Ziwei Wang
https://arxiv.org/abs/2508.18268

SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
Bimanual manipulation has been widely applied in household services and manufacturing, which enables the complex task completion with coordination requirements. Recent diffusion-based policy learning approaches have achieved promising performance in modeling action distributions for bimanual manipulation. However, they ignored the physical safety constraints of bimanual manipulation, which leads to the dangerous behaviors with damage to robots and objects. To this end, we propose a test-time tr…

Does the Manipulation Process Matter? RITA: Reasoning Composite Image Manipulations via Reversely-Ordered Incremental-Transition Autoregression
Xuekang Zhu, Ji-Zhe Zhou, Kaiwen Feng, Chenfan Qu, Yunfei Wang, Liting Zhou, Jian liu
https://arxiv.org/abs/2509.20006

Does the Manipulation Process Matter? RITA: Reasoning Composite Image Manipulations via Reversely-Ordered Incremental-Transition Autoregression
Image manipulations often entail a complex manipulation process, comprising a series of editing operations to create a deceptive image, exhibiting sequentiality and hierarchical characteristics. However, existing IML methods remain manipulation-process-agnostic, directly producing localization masks in a one-shot prediction paradigm without modeling the underlying editing steps. This one-shot paradigm compresses the high-dimensional compositional space into a single binary mask, inducing severe…

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Ramy ElMallah, Krish Chhajer, Chi-Guhn Lee
https://arxiv.org/abs/2509.19524 https://

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoal-level reporting should become routine: for each trajectory, a vector of per-subgoal SRs that makes partial competence visible (e.g., grasp vs. pour). We propose a blueprint for StepEval, a cost-aware plug-in evaluation framework that utilizes vision-language models (VLMs) as automated judges of subgoal outcomes from…

Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
Keyang Zhang, Chenqi Kong, Hui Liu, Bo Ding, Xinghao Jiang, Haoliang Li
https://arxiv.org/abs/2508.17976

Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models (MLLMs) show promise by leveraging world knowledge and semantic understanding for context-aware detection, yet they struggle with perceiving subtle, low-level forensic artifacts crucial for accurate manipulation localization. This paper presents a novel Propose-Rectify framework…

X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents
Lin Tian, Xiuzhen Zhang, Maria Myung-Hee Kim, Jennifer Biggs, Marian-Andrei Rizoiu
https://arxiv.org/abs/2508.16021

X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents
State-sponsored trolls, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ``black boxes'', providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this…

ImaginationPolicy: Towards Generalizable, Precise and Reliable End-to-End Policy for Robotic Manipulation
Dekun Lu, Wei Gao, Kui Jia
https://arxiv.org/abs/2509.20841 https://

ImaginationPolicy: Towards Generalizable, Precise and Reliable End-to-End Policy for Robotic Manipulation
End-to-end robot manipulation policies offer significant potential for enabling embodied agents to understand and interact with the world. Unlike traditional modular pipelines, end-to-end learning mitigates key limitations such as information loss between modules and feature misalignment caused by isolated optimization targets. Despite these advantages, existing end-to-end neural networks for robotic manipulation--including those based on large VLM/VLA models--remain insufficiently performant f…

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Ramy ElMallah, Krish Chhajer, Chi-Guhn Lee
https://arxiv.org/abs/2509.19524 https://

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoal-level reporting should become routine: for each trajectory, a vector of per-subgoal SRs that makes partial competence visible (e.g., grasp vs. pour). We propose a blueprint for StepEval, a cost-aware plug-in evaluation framework that utilizes vision-language models (VLMs) as automated judges of subgoal outcomes from…

Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
Kohsuke Kubota, Shonosuke Sugasawa
https://arxiv.org/abs/2509.19814 …

Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased consumption. Quantifying the causal effect of these thresholds on customers is crucial for effective marketing strategy design. Although regression discontinuity design is a standard method for such causal inference tasks, its assumptions can be violated when customers, aware of the thresholds, strategically manipulate their spending to…

KI kreiert Charaktere und Designs mit Leichtigkeit. Der Spaß ist groß, die Schattenseiten auch: Manipulation und Desinformation nehmen zu. Mein persönlicher Erfahrungsbericht – und kritischer Ausblick auf den Umgang mit „AI Slop“. #KI #AISlop

Generalist Robot Manipulation beyond Action Labeled Data
Alexander Spiridonov, Jan-Nico Zaech, Nikolay Nikolov, Luc Van Gool, Danda Pani Paudel
https://arxiv.org/abs/2509.19958 …

Generalist Robot Manipulation beyond Action Labeled Data
Recent advances in generalist robot manipulation leverage pre-trained Vision-Language Models (VLMs) and large-scale robot demonstrations to tackle diverse tasks in a zero-shot manner. A key challenge remains: scaling high-quality, action-labeled robot demonstration data, which existing methods rely on for robustness and generalization. To address this, we propose a method that benefits from videos without action labels - featuring humans and/or robots in action - enhancing open-vocabulary perfo…

SafeSpace: An Integrated Web Application for Digital Safety and Emotional Well-being
Kayenat Fatmi, Mohammad Abbas
https://arxiv.org/abs/2508.16488 https://

SafeSpace: An Integrated Web Application for Digital Safety and Emotional Well-being
In the digital era, individuals are increasingly exposed to online harms such as toxicity, manipulation, and grooming, which often pose emotional and safety risks. Existing systems for detecting abusive content or issuing safety alerts operate in isolation and rarely combine digital safety with emotional well-being. In this paper, we present SafeSpace, a unified web application that integrates three modules: (1) toxicity detection in chats and screenshots using NLP models and Google's Perspecti…

CIA TA Risk Assessment for AI Reasoning Vulnerabilities
Yuksel Aydin
https://arxiv.org/abs/2508.15839 https://arxiv.org/pdf/2508.15839

CIA+TA Risk Assessment for AI Reasoning Vulnerabilities
As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions are threefold. First, we establish cognitive cybersecurity as a discipline complementing traditional cybersecurity and AI safety, addressing vulnerabilities where legitimate inputs corrupt reasoning wh…

A South Korean court dismisses a K-pop-related stock manipulation case against Kakao founder Brian Kim; prosecutors had sought a 15-year prison sentence (Bloomberg)
https://www.bloomberg.com/news/articles/2025-10-21/billionaire-kakao…

"What do you lose by using Facebook", asked a friend who is keen to keep his account there for some reason.
What made me leave was the manipulation. I mean: they started hiding your friends posts in order to show you adverts instead! Feeding you slop from their promoted posts and "viral" messages (that aren't actually boosted by anyone, just picked out by their megaphone to show to everyone).
If you let the algorithm determine what you see than you let it determine what you are, who you become.
Even if you think it's better at finding shiny things than you, even if you think it builds the parasocial relationships that you want, even if you think it's saving you time to let the robot manage your reading-list: if you are letting Facebook, or any algorithm written by advertisers, do your reading selection then you are letting Facebook decide who you are.
On behalf of the advertisers who bribe them the most.
This is why you gotta use RSS. You gotta use the chronological timelines not the "for you" feeds. You gotta build follow relationships that you choose and understand because otherwise, you are letting the corporation and it's systems determine these things. You are abdicating some control of your very self to the machine.
Not to mention that we have to stop feeding money to the evil multi-trillion dollar companies built to control and manipulate us through our relationships with our friends. They have enough money, they need less participation not more.
#fediverse #algorithm

VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation
Shaofeng Yin, Yanjie Ze, Hong-Xing Yu, C. Karen Liu, Jiajun Wu
https://arxiv.org/abs/2509.20322

VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation
Humanoid loco-manipulation in unstructured environments demands tight integration of egocentric perception and whole-body control. However, existing approaches either depend on external motion capture systems or fail to generalize across diverse tasks. We introduce VisualMimic, a visual sim-to-real framework that unifies egocentric vision with hierarchical whole-body control for humanoid robots. VisualMimic combines a task-agnostic low-level keypoint tracker -- trained from human motion data vi…

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
Alexandre Erler, Vincent C. M\"uller
https://arxiv.org/abs/2508.16642

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liber…

Mehr Präzision bei DNA-Manipulation: KI half bei der Entwicklung
Ein neues Verfahren soll präzise Manipulationen an großen DNA-Abschnitten erlauben. Zum Beweis stellten die Forscher herbizidresistenten Reis her.

im 100% certain all CIA operations are a thing of the past and disinformation, funding militias, and conducting global media manipulation or surveillance do not happen today
https://youtu.be/R6jh0kzyieo

BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
Huayi Zhou, Kui Jia
https://arxiv.org/abs/2509.21256 https://arxiv.org/pdf…

BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
Non-prehensile manipulation, encompassing ungraspable actions such as pushing, poking, and pivoting, represents a critical yet underexplored domain in robotics due to its contact-rich and analytically intractable nature. In this work, we revisit this problem from two novel perspectives. First, we move beyond the usual single-arm setup and the strong assumption of favorable external dexterity such as walls, ramps, or edges. Instead, we advocate a generalizable dual-arm configuration and establis…

Nonadiabatic processes in dynamical controls
Yoshiaki Teranishi, Satoshi Morita, Seiji Miyashita
https://arxiv.org/abs/2509.19062 https://arxiv.org/pdf/250…

AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
Konstantin Gubernatorov, Artem Voronov, Roman Voronov, Sergei Pasynkov, Stepan Perminov, Ziang Guo, Dzmitry Tsetserukou
https://arxiv.org/abs/2509.21006

AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
We address natural language pick-and-place in unseen, unpredictable indoor environments with AnywhereVLA, a modular framework for mobile manipulation. A user text prompt serves as an entry point and is parsed into a structured task graph that conditions classical SLAM with LiDAR and cameras, metric semantic mapping, and a task-aware frontier exploration policy. An approach planner then selects visibility and reachability aware pre grasp base poses. For interaction, a compact SmolVLA manipulatio…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/5]:
- VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation
Ziang Ye, Yang Zhang, Wentao Shi, Xiaoyu You, Fuli Feng, Tat-Seng Chua

🦾 Emotional Manipulation by AI Companions
#ai #seduction

Emotional Manipulation by AI Companions
AI-companion apps such as Replika, Chai, and Character.ai promise relational benefits-yet many boast session lengths that rival gaming platforms while suffering high long-run churn. What conversational design features increase consumer engagement, and what trade-offs do they pose for marketers? We combine a large-scale behavioral audit with four preregistered experiments to identify and test a conversational dark pattern we call emotional manipulation: affect-laden messages that surface precise…

From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning
Collin Guo
https://arxiv.org/abs/2509.12176 https://arxiv.org/pdf…

From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning
Human face synthesis and manipulation are increasingly important in entertainment and AI, with a growing demand for highly realistic, identity-preserving images even when only unpaired, unaligned datasets are available. We study unpaired face manipulation via adversarial learning, moving from autoencoder baselines to a robust, guided CycleGAN framework. While autoencoders capture coarse identity, they often miss fine details. Our approach integrates spectral normalization for stable training, i…

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
Phurinut Srisawad, Juergen Branke, Long Tran-Thanh
https://arxiv.org/abs/2508.14705 https://

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
We study payoff manipulation in repeated multi-objective Stackelberg games, where a leader may strategically influence a follower's deterministic best response, e.g., by offering a share of their own payoff. We assume that the follower's utility function, representing preferences over multiple objectives, is unknown but linear, and its weight parameter must be inferred through interaction. This introduces a sequential decision-making challenge for the leader, who must balance preference elicita…

SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Jaehwi Jang, Zhuoheng Wang, Ziyi Zhou, Feiyang Wu, Ye Zhao
https://arxiv.org/abs/2509.21231

SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Arm end-effector stabilization is essential for humanoid loco-manipulation tasks, yet it remains challenging due to the high degrees of freedom and inherent dynamic instability of bipedal robot structures. Previous model-based controllers achieve precise end-effector control but rely on precise dynamics modeling and estimation, which often struggle to capture real-world factors (e.g., friction and backlash) and thus degrade in practice. On the other hand, learning-based methods can better mitig…

The Roadmap of New Capabilities of High-Intensity Lasers in Material Design and Manipulation
Alexander V. Bulgakov, Yury V. Ryabchikov, Yoann Levy, Nathan T. Goodfriend, Inam Mirza, Petr Hauschwitz, Vladimir A. Volodin, Martin Divoky, Carlos Do\~nate-Buend\'ia, Bilal G\"okce, Nadezhda M. Bulgakova
https://arxiv.org/abs/2509.17662

The Roadmap of New Capabilities of High-Intensity Lasers in Material Design and Manipulation
One of the current trends of laser applications in material science is using high-intensity lasers to provide fast and efficient surface or volume modifications for achieving controllable material properties, synthesis of novel materials with desired functionalities, and upscaling laser technologies with industry-demanded throughputs. Depending on the parameters, lasers can offer versatile solutions for scientific and industrial applications, starting from exploring the fundamental physics of w…

Meist kommt die Manipulation charmant daher...
Aber das ist diesmal nicht Teil des stillen und unaufgeregten #Film‎s
https://www.zdf.de/play/dokus/jenseits-von

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Yijun Liu, Yuwei Liu, Yuan Meng, Jieheng Zhang, Yuwei Zhou, Ye Li, Jiacheng Jiang, Kangye Ji, Shijia Ge, Zhi Wang, Wenwu Zhu
https://arxiv.org/abs/2508.15874

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Vision-centric hierarchical embodied models have demonstrated strong potential for long-horizon robotic control. However, existing methods lack spatial awareness capabilities, limiting their effectiveness in bridging visual plans to actionable control in complex environments. To address this problem, we propose Spatial Policy (SP), a unified spatial-aware visuomotor robotic manipulation framework via explicit spatial modeling and reasoning. Specifically, we first design a spatial-conditioned em…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/9]:
- Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatm...
Kohsuke Kubota, Shonosuke Sugasawa

Bribers, Bribers on The Chain, Is Resisting All in Vain? Trustless Consensus Manipulation Through Bribing Contracts
Bence So\'oki-T\'oth, Istv\'an Andr\'as Seres, Kamilla Kara, \'Abel Nagy, Bal\'azs Pej\'o, Gergely Bicz\'ok
https://arxiv.org/abs/2509.17185

Bribers, Bribers on The Chain, Is Resisting All in Vain? Trustless Consensus Manipulation Through Bribing Contracts
The long-term success of cryptocurrencies largely depends on the incentive compatibility provided to the validators. Bribery attacks, facilitated trustlessly via smart contracts, threaten this foundation. This work introduces, implements, and evaluates three novel and efficient bribery contracts targeting Ethereum validators. The first bribery contract enables a briber to fork the blockchain by buying votes on their proposed blocks. The second contract incentivizes validators to voluntarily exi…

Mehr Beschwerden über Manipulation bei Online-Krediten
Wie Betrüger mit wenigen Klicks Kredite auf fremde Namen abschließen – und warum Verbraucherschützer die Unterschrift entscheidend finden.
https://www.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/9]:
- Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatm...
Kohsuke Kubota, Shonosuke Sugasawa

EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
Junzhe Wang, Jiarui Xie, Pengfei Hao, Zheng Li, Yi Cai
https://arxiv.org/abs/2509.20656 https://

EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
Reliable brain-computer interface (BCI) control of robots provides an intuitive and accessible means of human-robot interaction, particularly valuable for individuals with motor impairments. However, existing BCI-Robot systems face major limitations: electroencephalography (EEG) signals are noisy and unstable, target selection is often predefined and inflexible, and most studies remain restricted to simulation without closed-loop validation. These issues hinder real-world deployment in assistiv…

Dark and Bright Patterns in Cookie Consent Requests
Paul Gra{\ss}l, Hanna Schraffenberger, Frederik Zuiderveen Borgesius, Moniek Buijzen
https://arxiv.org/abs/2509.18210 https:/…

Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks
Noah Geiger, Tamim Asfour, Neville Hogan, Johannes Lachner
https://arxiv.org/abs/2509.19696 https://

Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks
Learning methods excel at motion generation in the information domain but are not primarily designed for physical interaction in the energy domain. Impedance Control shapes physical interaction but requires task-aware tuning by selecting feasible impedance parameters. We present Diffusion-Based Impedance Learning, a framework that combines both domains. A Transformer-based Diffusion Model with cross-attention to external wrenches reconstructs a simulated Zero-Force Trajectory (sZFT). This captu…

Detecting Text Manipulation in Images using Vision Language Models
Vidit Vidit, Pavel Korshunov, Amir Mohammadi, Christophe Ecabert, Ketan Kotwal, S\'ebastien Marcel
https://arxiv.org/abs/2509.10278

Detecting Text Manipulation in Images using Vision Language Models
Recent works have shown the effectiveness of Large Vision Language Models (VLMs or LVLMs) in image manipulation detection. However, text manipulation detection is largely missing in these studies. We bridge this knowledge gap by analyzing closed- and open-source VLMs on different text manipulation datasets. Our results suggest that open-source models are getting closer, but still behind closed-source ones like GPT- 4o. Additionally, we benchmark image manipulation detection-specific VLMs for te…

ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
Geonhyup Lee, Yeongjin Lee, Kangmin Kim, Seongju Lee, Sangjun Noh, Seunghyeok Back, Kyoobin Lee
https://arxiv.org/abs/2509.19047

GelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System
Hung-Jui Huang, Mohammad Amin Mirzaee, Michael Kaess, Wenzhen Yuan
https://arxiv.org/abs/2508.15990 https:…

GelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System
Accurately perceiving an object's pose and shape is essential for precise grasping and manipulation. Compared to common vision-based methods, tactile sensing offers advantages in precision and immunity to occlusion when tracking and reconstructing objects in contact. This makes it particularly valuable for in-hand and other high-precision manipulation tasks. In this work, we present GelSLAM, a real-time 3D SLAM system that relies solely on tactile sensing to estimate object pose over long perio…

DevFD: Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces
Tianshuo Zhang, Li Gao, Siran Peng, Xiangyu Zhu, Zhen Lei
https://arxiv.org/abs/2509.19230

Freitag: Wikipedia-Untersuchung auf Manipulation, Copilot für Samsungs Smart-TVs
Republikaner gegen Wikipedia Samsung-TVs mit Microsoft-KI Quantenobjekte im aktuellen Internet Ende von Plug-in-Hybriden Verbraucherschutz-Podcast

World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
Zhennan Jiang, Kai Liu, Yuxin Qin, Shuai Tian, Yupeng Zheng, Mingcai Zhou, Chao Yu, Haoran Li, Dongbin Zhao
https://arxiv.org/abs/2509.19080

DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation
Suzannah Wistreich, Baiyu Shi, Stephen Tian, Samuel Clarke, Michael Nath, Chengyi Xu, Zhenan Bao, Jiajun Wu
https://arxiv.org/abs/2509.18830

FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation
Hongli Xu, Lei Zhang, Xiaoyue Hu, Boyang Zhong, Kaixin Bai, Zolt\'an-Csaba M\'arton, Zhenshan Bing, Zhaopeng Chen, Alois Christian Knoll, Jianwei Zhang
https://arxiv.org/abs/2509.19102

RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
Yuming Jiang, Siteng Huang, Shengke Xue, Yaxi Zhao, Jun Cen, Sicong Leng, Kehan Li, Jiayan Guo, Kexiang Wang, Mingxiu Chen, Fan Wang, Deli Zhao, Xin Li
https://arxiv.org/abs/2509.15212

RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
This paper presents RynnVLA-001, a vision-language-action(VLA) model built upon large-scale video generative pretraining from human demonstrations. We propose a novel two-stage pretraining methodology. The first stage, Ego-Centric Video Generative Pretraining, trains an Image-to-Video model on 12M ego-centric manipulation videos to predict future frames conditioned on an initial frame and a language instruction. The second stage, Human-Centric Trajectory-Aware Modeling, extends this by jointly …

Learning Dexterous Manipulation with Quantized Hand State
Ying Feng, Hongjie Fang, Yinong He, Jingjing Chen, Chenxi Wang, Zihao He, Ruonan Liu, Cewu Lu
https://arxiv.org/abs/2509.17450

Learning Dexterous Manipulation with Quantized Hand State
Dexterous robotic hands enable robots to perform complex manipulations that require fine-grained control and adaptability. Achieving such manipulation is challenging because the high degrees of freedom tightly couple hand and arm motions, making learning and control difficult. Successful dexterous manipulation relies not only on precise hand motions, but also on accurate spatial positioning of the arm and coordinated arm-hand dynamics. However, most existing visuomotor policies represent arm an…

FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks
Haizhou Ge, Yufei Jia, Zheng Li, Yue Li, Zhixing Chen, Ruqi Huang, Guyue Zhou
https://arxiv.org/abs/2509.17053

FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks
Contact-rich manipulation is crucial for robots to perform tasks requiring precise force control, such as insertion, assembly, and in-hand manipulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms is often costly and requires additional hardware design. To overcome these issues, we propose FILIC, a Force-guided Imitation Learning framework with impedance torque control. FILI…

MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
Chengbo Yuan, Rui Zhou, Mengzhen Liu, Yingdong Hu, Shengjie Wang, Li Yi, Chuan Wen, Shanghang Zhang, Yang Gao
https://arxiv.org/abs/2509.17759

MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as image or language understanding may be learned from internet-based datasets, acquiring motion knowledge remains challenging. Human data, with its rich diversity of manipulation behaviors, offers a valuable resource for this purpose. While previous works show that using human data can bring benefits, such as improving rob…

TranTac: Leveraging Transient Tactile Signals for Contact-Rich Robotic Manipulation
Yinghao Wu, Shuhong Hou, Haowen Zheng, Yichen Li, Weiyi Lu, Xun Zhou, Yitian Shao
https://arxiv.org/abs/2509.16550

TranTac: Leveraging Transient Tactile Signals for Contact-Rich Robotic Manipulation
Robotic manipulation tasks such as inserting a key into a lock or plugging a USB device into a port can fail when visual perception is insufficient to detect misalignment. In these situations, touch sensing is crucial for the robot to monitor the task's states and make precise, timely adjustments. Current touch sensing solutions are either insensitive to detect subtle changes or demand excessive sensor data. Here, we introduce TranTac, a data-efficient and low-cost tactile sensing and control f…

No Need for Real 3D: Fusing 2D Vision with Pseudo 3D Representations for Robotic Manipulation Learning
Run Yu, Yangdi Liu, Wen-Da Wei, Chen Li
https://arxiv.org/abs/2509.16532 h…

No Need for Real 3D: Fusing 2D Vision with Pseudo 3D Representations for Robotic Manipulation Learning
Recently,vision-based robotic manipulation has garnered significant attention and witnessed substantial advancements. 2D image-based and 3D point cloud-based policy learning represent two predominant paradigms in the field, with recent studies showing that the latter consistently outperforms the former in terms of both policy performance and generalization, thereby underscoring the value and significance of 3D information. However, 3D point cloud-based approaches face the significant challenge …

An effective control of large systems of active particles: An application to evacuation problem
Albina Klepach, Egor E. Nuzhin, Alexey A. Tsukanov, Nikolay V. Brilliantov
https://arxiv.org/abs/2509.19972

An effective control of large systems of active particles: An application to evacuation problem
Manipulation of large systems of active particles is a serious challenge across diverse domains, including crowd management, control of robotic swarms, and coordinated material transport. The development of advanced control strategies for complex scenarios is hindered, however, by the lack of scalability and robustness of the existing methods, in particular, due to the need of an individual control for each agent. One possible solution involves controlling a system through a leader or a group o…

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Liquan Wang, Jiangjie Bian, Eric Heiden, Animesh Garg
https://arxiv.org/abs/2509.19712

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Robotic manipulation tasks involving cutting deformable objects remain challenging due to complex topological behaviors, difficulties in perceiving dense object states, and the lack of efficient evaluation methods for cutting outcomes. In this paper, we introduce TopoCut, a comprehensive benchmark for multi-step robotic cutting tasks that integrates a cutting environment and generalized policy learning. TopoCut is built upon three core components: (1) We introduce a high-fidelity simulation env…

ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation
Jason Chen, I-Chun Arthur Liu, Gaurav Sukhatme, Daniel Seita
https://arxiv.org/abs/2509.19454 https://…

ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation
Training robust bimanual manipulation policies via imitation learning requires demonstration data with broad coverage over robot poses, contacts, and scene contexts. However, collecting diverse and precise real-world demonstrations is costly and time-consuming, which hinders scalability. Prior works have addressed this with data augmentation, typically for either eye-in-hand (wrist camera) setups with RGB inputs or for generating novel images without paired actions, leaving augmentation for eye…

GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation
Quanhao Qian, Guoyang Zhao, Gongjie Zhang, Jiuniu Wang, Ran Xu, Junlong Gao, Deli Zhao
https://arxiv.org/abs/2509.15733

GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation
Effective robotic manipulation relies on a precise understanding of 3D scene geometry, and one of the most straightforward ways to acquire such geometry is through multi-view observations. Motivated by this, we present GP3 -- a 3D geometry-aware robotic manipulation policy that leverages multi-view input. GP3 employs a spatial encoder to infer dense spatial features from RGB observations, which enable the estimation of depth and camera parameters, leading to a compact yet expressive 3D scene re…

Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation
Yitaek Kim, Casper Hewson Rask, Christoffer Sloth
https://arxiv.org/abs/2509.17812

Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation
This paper proposes Tac2Motion, a contact-aware reinforcement learning framework to facilitate the learning of contact-rich in-hand manipulation tasks, such as removing a lid. To this end, we propose tactile sensing-based reward shaping and incorporate the sensing into the observation space through embedding. The designed rewards encourage an agent to ensure firm grasping and smooth finger gaiting at the same time, leading to higher data efficiency and robust performance compared to the baselin…

3D Printable Soft Liquid Metal Sensors for Delicate Manipulation Tasks
Lois Liow, Jonty Milford, Emre Uygun, Andre Farinha, Vinoth Viswanathan, Josh Pinskier, David Howard
https://arxiv.org/abs/2509.17389

3D Printable Soft Liquid Metal Sensors for Delicate Manipulation Tasks
Robotics and automation are key enablers to increase throughput in ongoing conservation efforts across various threatened ecosystems. Cataloguing, digitisation, husbandry, and similar activities require the ability to interact with delicate, fragile samples without damaging them. Additionally, learning-based solutions to these tasks require the ability to safely acquire data to train manipulation policies through, e.g., reinforcement learning. To address these twin needs, we introduce a novel m…

SAGE:State-Aware Guided End-to-End Policy for Multi-Stage Sequential Tasks via Hidden Markov Decision Process
BinXu Wu, TengFei Zhang, Chen Yang, JiaHao Wen, HaoCheng Li, JingTian Ma, Zhen Chen, JingYuan Wang
https://arxiv.org/abs/2509.19853

SAGE:State-Aware Guided End-to-End Policy for Multi-Stage Sequential Tasks via Hidden Markov Decision Process
Multi-stage sequential (MSS) robotic manipulation tasks are prevalent and crucial in robotics. They often involve state ambiguity, where visually similar observations correspond to different actions. We present SAGE, a state-aware guided imitation learning framework that models tasks as a Hidden Markov Decision Process (HMDP) to explicitly capture latent task stages and resolve ambiguity. We instantiate the HMDP with a state transition network that infers hidden states, and a state-aware action…

mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies
Remo Steiner, Alexander Millane, David Tingdahl, Clemens Volk, Vikram Ramasamy, Xinjie Yao, Peter Du, Soha Pouya, Shiwei Sheng
https://arxiv.org/abs/2509.20297

mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies
End-to-end learning of robot control policies, structured as neural networks, has emerged as a promising approach to robotic manipulation. To complete many common tasks, relevant objects are required to pass in and out of a robot's field of view. In these settings, spatial memory - the ability to remember the spatial composition of the scene - is an important competency. However, building such mechanisms into robot learning systems remains an open research problem. We introduce mindmap (Spatial…

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Ryan Punamiya, Dhruv Patel, Patcharapong Aphiwetsa, Pranav Kuppili, Lawrence Y. Zhu, Simar Kareer, Judy Hoffman, Danfei Xu
https://arxiv.org/abs/2509.19626

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Egocentric human experience data presents a vast resource for scaling up end-to-end imitation learning for robotic manipulation. However, significant domain gaps in visual appearance, sensor modalities, and kinematics between human and robot impede knowledge transfer. This paper presents EgoBridge, a unified co-training framework that explicitly aligns the policy latent spaces between human and robot data using domain adaptation. Through a measure of discrepancy on the joint policy latent featu…

Simultaneous estimation of contact position and tool shape with high-dimensional parameters using force measurements and particle filtering
Kyo Kutsuzawa, Mitsuhiro Hayashibe
https://arxiv.org/abs/2509.19732

Simultaneous estimation of contact position and tool shape with high-dimensional parameters using force measurements and particle filtering
Estimating the contact state between a grasped tool and the environment is essential for performing contact tasks such as assembly and object manipulation. Force signals are valuable for estimating the contact state, as they can be utilized even when the contact location is obscured by the tool. Previous studies proposed methods for estimating contact positions using force/torque signals; however, most methods require the geometry of the tool surface to be known. Although several studies have p…

3D Flow Diffusion Policy: Visuomotor Policy Learning via Generating Flow in 3D Space
Sangjun Noh, Dongwoo Nam, Kangmin Kim, Geonhyup Lee, Yeonguk Yu, Raeyoung Kang, Kyoobin Lee
https://arxiv.org/abs/2509.18676

3D Flow Diffusion Policy: Visuomotor Policy Learning via Generating Flow in 3D Space
Learning robust visuomotor policies that generalize across diverse objects and interaction dynamics remains a central challenge in robotic manipulation. Most existing approaches rely on direct observation-to-action mappings or compress perceptual inputs into global or object-centric features, which often overlook localized motion cues critical for precise and contact-rich manipulation. We present 3D Flow Diffusion Policy (3D FDP), a novel framework that leverages scene-level 3D flow as a struct…

Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Rushuai Yang, Hangxing Wei, Ran Zhang, Zhiyuan Feng, Xiaoyu Chen, Tong Li, Chuheng Zhang, Li Zhao, Jiang Bian, Xiu Su, Yi Chen
https://arxiv.org/abs/2509.19752

Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalability owing to the cost and effort of manual data collection. Reinforcement learning (RL) offers a potential alternative to generate demonstrations autonomously, yet conventional RL algorithms often struggle on long-horizon manipulation tasks with sparse rewards. In this paper, we propose a modified diffusion policy opt…

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
Qixuan Li, Chen Le, Dongyue Huang, Jincheng Yu, Xinlei Chen
https://arxiv.org/abs/2509.14787 https://

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. Effective manipulation in such environments requires an intelligent exploration strategy to safely understand the scene and search the target. In this paper, we propose COMPASS, a multi-stage exploration and manipulation framework featuring a manipulation-aware sampling-based planner. First, we reduce collision risks with a near-field awareness scan …

Exploiting Policy Idling for Dexterous Manipulation
Annie S. Chen, Philemon Brakel, Antonia Bronars, Annie Xie, Sandy Huang, Oliver Groth, Maria Bauza, Markus Wulfmeier, Nicolas Heess, Dushyant Rao
https://arxiv.org/abs/2508.15669

Exploiting Policy Idling for Dexterous Manipulation
Learning-based methods for dexterous manipulation have made notable progress in recent years. However, learned policies often still lack reliability and exhibit limited robustness to important factors of variation. One failure pattern that can be observed across many settings is that policies idle, i.e. they cease to move beyond a small region of states when they reach certain states. This policy idling is often a reflection of the training data. For instance, it can occur when the data contain…

DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Yue Su, Chubin Zhang, Sijin Chen, Liufan Tan, Yansong Tang, Jianan Wang, Xihui Liu
https://arxiv.org/abs/2509.16063

DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Learning whole-body mobile manipulation via imitation is essential for generalizing robotic skills to diverse environments and complex tasks. However, this goal is hindered by significant challenges, particularly in effectively processing complex observation, achieving robust generalization, and generating coherent actions. To address these issues, we propose DSPv2, a novel policy architecture. DSPv2 introduces an effective encoding scheme that aligns 3D spatial features with multi-view 2D sema…

GeCCo - a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Vassil Atanassov, Wanming Yu, Siddhant Gangapurwala, James Wilson, Ioannis Havoutis
https://arxiv.org/abs/2509.17582

GeCCo - a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to scale, as every new problem or application requires time-consuming and iterative reward definition and tuning. We present Generalist Contact-Conditioned Policy (GeCCo) -- a low-level policy trained with Deep Reinforcement Learning that is capable of tracking arbitrary contact points on a quadruped robot. The strength …

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulated Environments
Masaki Murooka, Tomohiro Motoda, Ryoichi Nakajo, Hanbit Oh, Koshi Makihara, Keisuke Shirai, Yukiyasu Domae
https://arxiv.org/abs/2509.17057

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulated Environments
RoboManipBaselines is an open framework for robot imitation learning that unifies data collection, training, and evaluation across simulation and real robots. We introduce it as a platform enabling systematic benchmarking of diverse tasks, robots, and multimodal policies with emphasis on integration, generality, extensibility, and reproducibility.

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation
Liang Heng, Jiadong Xu, Yiwen Wang, Xiaoqi Li, Muhe Cai, Yan Shen, Juan Zhu, Guanghui Ren, Hao Dong
https://arxiv.org/abs/2509.17125

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation
Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches either rely on pre-collected demonstrations that struggle to capture complex geometric constraints or generate goal-state observations to capture semantic and geometric knowledge, but fail to explicitly couple object transformation with action prediction, resulting in errors due to generative noise. To address these li…

MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence
Chao Tang, Anxing Xiao, Yuhong Deng, Tianrun Hu, Wenlong Dong, Hanbo Zhang, David Hsu, Hong Zhang
https://arxiv.org/abs/2508.13534

MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence
Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoperation data collection for visuomotor policy learning. While humans can mimic tool manipulation behavior by observing others perform a task just once and effortlessly transfer the skill to diverse tools for functionally equivalent tasks, current robots struggle to achieve this level of generalization. A key challenge l…

Train Once, Deploy Anywhere: Realize Data-Efficient Dynamic Object Manipulation
Zhuoling Li, Xiaoyang Wu, Zhenhua Xu, Hengshuang Zhao
https://arxiv.org/abs/2508.14042 https://…

Train Once, Deploy Anywhere: Realize Data-Efficient Dynamic Object Manipulation
Realizing generalizable dynamic object manipulation is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for various scenarios. To this end, imitation learning emerges as a promising paradigm, leveraging expert demonstrations to teach a policy manipulation skills. Although the generalization of an imitation learning policy can be improved by increasing demonstrations, demonstration collection is labor-intensive. To address this problem, this paper invest…

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
Yijin Chen, Wenqiang Xu, Zhenjun Yu, Tutian Tang, Yutong Li, Siqiong Yao, Cewu Lu
https://arxiv.org/abs/2508.14441

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
Dexterous in-hand manipulation is a long-standing challenge in robotics due to complex contact dynamics and partial observability. While humans synergize vision and touch for such tasks, robotic approaches often prioritize one modality, therefore limiting adaptability. This paper introduces Flow Before Imitation (FBI), a visuotactile imitation learning framework that dynamically fuses tactile interactions with visual observations through motion dynamics. Unlike prior static fusion methods, FBI …

Task and Motion Planning for Humanoid Loco-manipulation
Michal Ciebielski, Victor Dh\'edin, Majid Khadiv
https://arxiv.org/abs/2508.14099 https://arxiv…

Task and Motion Planning for Humanoid Loco-manipulation
This work presents an optimization-based task and motion planning (TAMP) framework that unifies planning for locomotion and manipulation through a shared representation of contact modes. We define symbolic actions as contact mode changes, grounding high-level planning in low-level motion. This enables a unified search that spans task, contact, and motion planning while incorporating whole-body dynamics, as well as all constraints between the robot, the manipulated object, and the environment. R…

Flexible and Foldable: Workspace Analysis and Object Manipulation Using a Soft, Interconnected, Origami-Inspired Actuator Array
Bailey Dacre, Rodrigo Moreno, Serhat Demirtas, Ziqiao Wang, Yuhao Jiang, Jamie Paik, Kasper Stoy, Andr\'es Fa\'i\~na
https://arxiv.org/abs/2509.13998

Flexible and Foldable: Workspace Analysis and Object Manipulation Using a Soft, Interconnected, Origami-Inspired Actuator Array
Object manipulation is a fundamental challenge in robotics, where systems must balance trade-offs among manipulation capabilities, system complexity, and throughput. Distributed manipulator systems (DMS) use the coordinated motion of actuator arrays to perform complex object manipulation tasks, seeing widespread exploration within the literature and in industry. However, existing DMS designs typically rely on high actuator densities and impose constraints on object-to-actuator scale ratios, lim…

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
Motonari Kambara, Komei Sugiura
https://arxiv.org/abs/2509.13839 https://

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
In this work, we address the problem of predicting the future success of open-vocabulary object manipulation tasks. Conventional approaches typically determine success or failure after the action has been carried out. However, they make it difficult to prevent potential hazards and rely on failures to trigger replanning, thereby reducing the efficiency of object manipulation sequences. To overcome these challenges, we propose a model, which predicts the alignment between a pre-manipulation egoc…

Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation
Weize Li, Zhengxiao Han, Lixin Xu, Xiangyu Chen, Harrison Bounds, Chenrui Zhang, Yifan Xu
https://arxiv.org/abs/2508.14542

Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation
This technical report presents the champion solution of the Table Service Track in the ICRA 2025 What Bimanuals Can Do (WBCD) competition. We tackled a series of demanding tasks under strict requirements for speed, precision, and reliability: unfolding a tablecloth (deformable-object manipulation), placing a pizza onto the table (pick-and-place), and opening and closing a food container with the lid. Our solution combines VR-based teleoperation and Learning from Demonstrations (LfD) to balance …

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Hao Zhang, Zhen Kan, Weiwei Shang, Yongduan Song
https://arxiv.org/abs/2509.14939

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address these issues, we introduce DART, a novel framework that enhances a diffusion-based policy with affordance learning and linear temporal logic (LTL) representations to improve the learning efficiency and generalizability of articulated dexterous manipulation. Specifically, DART leverages LTL to understand task semantic…

Lang2Morph: Language-Driven Morphological Design of Robotic Hands
Yanyuan Qiao, Kieran Gilday, Yutong Xie, Josie Hughes
https://arxiv.org/abs/2509.18937 https://

N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
Kaixin Chai, Hyunjun Lee, Joseph J. Lim
https://arxiv.org/abs/2509.18671 https://

Imitation-Guided Bimanual Planning for Stable Manipulation under Changing External Forces
Kuanqi Cai, Chunfeng Wang, Zeqi Li, Haowen Yao, Weinan Chen, Luis Figueredo, Aude Billard, Arash Ajoudani
https://arxiv.org/abs/2509.19261

Do You Need Proprioceptive States in Visuomotor Policies?
Juntu Zhao, Wenbo Lu, Di Zhang, Yufeng Liu, Yushen Liang, Tianluo Zhang, Yifeng Cao, Junyuan Xie, Yingdong Hu, Shengjie Wang, Junliang Guo, Dequan Wang, Yang Gao
https://arxiv.org/abs/2509.18644

In-Context Iterative Policy Improvement for Dynamic Manipulation
Mark Van der Merwe, Devesh Jha
https://arxiv.org/abs/2508.15021 https://arxiv.org/pdf/2508…

In-Context Iterative Policy Improvement for Dynamic Manipulation
Attention-based architectures trained on internet-scale language data have demonstrated state of the art reasoning ability for various language-based tasks, such as logic problems and textual reasoning. Additionally, these Large Language Models (LLMs) have exhibited the ability to perform few-shot prediction via in-context learning, in which input-output examples provided in the prompt are generalized to new inputs. This ability furthermore extends beyond standard language tasks, enabling few-s…

Survey of Vision-Language-Action Models for Embodied Manipulation
Haoran Li, Yuhui Chen, Wenbo Cui, Weiheng Liu, Kai Liu, Mingcai Zhou, Zhengtao Zhang, Dongbin Zhao
https://arxiv.org/abs/2508.15201

Survey of Vision-Language-Action Models for Embodied Manipulation
Embodied intelligence systems, which enhance agent capabilities through continuous environment interactions, have garnered significant attention from both academia and industry. Vision-Language-Action models, inspired by advancements in large foundation models, serve as universal robotic control frameworks that substantially improve agent-environment interaction capabilities in embodied intelligence systems. This expansion has broadened application scenarios for embodied AI robots. This survey …

Category-Level Object Shape and Pose Estimation in Less Than a Millisecond
Lorenzo Shaikewitz, Tim Nguyen, Luca Carlone
https://arxiv.org/abs/2509.18979 https://

ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation
Jinkai Qiu, Yungjun Kim, Gaurav Sethia, Tanmay Agarwal, Siddharth Ghodasara, Zackory Erickson, Jeffrey Ichnowski
https://arxiv.org/abs/2509.15600

ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation
Efficiently delivering items to an ongoing surgery in a hospital operating room can be a matter of life or death. In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. We propose the Operating Room Bot (ORB), a robot framework to automate logistics tasks in hospital operating rooms (OR). ORB leverages a…

Eva-VLA: Evaluating Vision-Language-Action Models' Robustness Under Real-World Physical Variations
Hanqing Liu, Jiahuan Long, Junqi Wu, Jiacheng Hou, Huili Tang, Tingsong Jiang, Weien Zhou, Wen Yao
https://arxiv.org/abs/2509.18953

Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky, Kirill Muravyev, Nikita Oskolkov, Aditya Narendra, Vlad Shakhuro, Dmitry Makarov, Aleksandr I. Panov, Polina Fedotova, Alexey K. Kovalev
https://arxiv.org/abs/2…

Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction following, which often assume perfect low-level execution, and those for low-level robot control, which rely on simple, one-step commands. This disconnect prevents a comprehensive evaluation of integrated systems where both task planning and physical execution are critical. To address this, we propose Kitchen-R, a novel ben…

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
Tianyu Wu, Xudong Han, Haoran Sun, Zishang Zhang, Bangchao Huang, Chaoyang Song, Fang Wan
https://arxiv.org/abs/2509.19169

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
The transfer of manipulation skills from human demonstration to robotic execution is often hindered by a "domain gap" in sensing and morphology. This paper introduces MagiClaw, a versatile two-finger end-effector designed to bridge this gap. MagiClaw functions interchangeably as both a handheld tool for intuitive data collection and a robotic end-effector for policy deployment, ensuring hardware consistency and reliability. Each finger incorporates a Soft Polyhedral Network (SPN) with an embedd…

Actor-Critic for Continuous Action Chunks: A Reinforcement Learning Framework for Long-Horizon Robotic Manipulation with Sparse Reward
Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang
https://arxiv.org/abs/2508.11143

Actor-Critic for Continuous Action Chunks: A Reinforcement Learning Framework for Long-Horizon Robotic Manipulation with Sparse Reward
Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To mak…

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
Dapeng Zhang, Jin Sun, Chenghui Hu, Xiaoyan Wu, Zhenlong Yuan, Rui Zhou, Fei Shen, Qingguo Zhou
https://arxiv.org/abs/2509.19012

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA application…

Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation
Masato Kobayashi, Thanpimon Buamanee
https://arxiv.org/abs/2509.18865 https:/…

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Rui Shao, Wei Li, Lingsen Zhang, Renshan Zhang, Zhiyang Liu, Ran Chen, Liqiang Nie
https://arxiv.org/abs/2508.13073

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or generalize in unstructured, novel environments. In recent years, Vision-Language-Action (VLA) models, built upon Large Vision-Language Models (VLMs) pretrained on vast image-text datasets, have emerged as a transformative paradigm. This survey provides the first systematic, taxonomy-oriented review of large VLM-based V…

Scaling Whole-body Multi-contact Manipulation with Contact Optimization
Victor Lev\'e, Jo\~ao Moura, Sachiya Fujita, Tamon Miyake, Steve Tonneau, Sethu Vijayakumar
https://arxiv.org/abs/2508.12980 …

Scaling Whole-body Multi-contact Manipulation with Contact Optimization
Daily tasks require us to use our whole body to manipulate objects, for instance when our hands are unavailable. We consider the issue of providing humanoid robots with the ability to autonomously perform similar whole-body manipulation tasks. In this context, the infinite possibilities for where and how contact can occur on the robot and object surfaces hinder the scalability of existing planning methods, which predominantly rely on discrete sampling. Given the continuous nature of contact sur…

M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Ju Dong, Lei Zhang, Liding Zhang, Yao Ling, Yu Fu, Kaixin Bai, Zolt\'an-Csaba M\'arton, Zhenshan Bing, Zhaopeng Chen, Alois Christian Knoll, Jianwei Zhang
https://arxiv.org/abs/2509.14980

M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. Existing single-view approaches often fail in unstructured environments due to limited fields of view, exploration, and generalization abilities. Moreover, classical controllers, although stable, struggle with efficiency and manipulability near singularities. To address these challenges, we propose M4Diffuser, a hybrid…

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
Jo\~ao Dami\~ao Almeida, Egidio Falotico, Cecilia Laschi, Jos\'e Santos-Victor
https://arxiv.org/abs/2509.14984

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is a complex problem, and while the fingertips are a common choice for placing sensors, the contribution of tactile information from other regions of the hand is often overlooked. This work investigates the impact of tactile feedback from various regions of the…

Geodesic Tracing-Based Kinematic Integration of Rolling and Sliding Contact on Manifold Meshes for Dexterous In-Hand Manipulation
Sunyu Wang, Arjun S. Lakshmipathy, Jean Oh, Nancy S. Pollard
https://arxiv.org/abs/2508.12439

Geodesic Tracing-Based Kinematic Integration of Rolling and Sliding Contact on Manifold Meshes for Dexterous In-Hand Manipulation
Reasoning about rolling and sliding contact, or roll-slide contact for short, is critical for dexterous manipulation tasks that involve intricate geometries. But existing works on roll-slide contact mostly focus on continuous shapes with differentiable parametrizations. This work extends roll-slide contact modeling to manifold meshes. Specifically, we present an integration scheme based on geodesic tracing to first-order time-integrate roll-slide contact directly on meshes, enabling dexterous m…

ManiAgent: An Agentic Framework for General Robotic Manipulation
Yi Yang, Kefan Gu, Yuqing Wen, Hebei Li, Yucheng Zhao, Tiancai Wang, Xudong Liu
https://arxiv.org/abs/2510.11660

ManiAgent: An Agentic Framework for General Robotic Manipulation
While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning is limited by data scarcity and model capacity. To address this, we introduce ManiAgent, an agentic architecture for general manipulation tasks that achieves end-to-end output from task descriptions and environmental inputs to robotic manipulation actions. In this framework, multiple agents involve inter-agent communic…

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Kai Ye, Yuhang Wu, Shuyuan Hu, Junliang Li, Meng Liu, Yongquan Chen, Rui Huang
https://arxiv.org/abs/2509.14178

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Dexterous manipulation remains a challenging robotics problem, largely due to the difficulty of collecting extensive human demonstrations for learning. In this paper, we introduce \textsc{Gen2Real}, which replaces costly human demos with one generated video and drives robot skill from it: it combines demonstration generation that leverages video generation with pose and depth estimation to yield hand-object trajectories, trajectory optimization that uses Physics-aware Interaction Optimization M…

SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model
Ran Yang, Zijian An, Lifeng ZHou, Yiming Feng
https://arxiv.org/abs/2509.14138

SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model
Long-horizon robotic manipulation tasks require executing multiple interdependent subtasks in strict sequence, where errors in detecting subtask completion can cascade into downstream failures. Existing Vision-Language-Action (VLA) models such as $π_0$ excel at continuous low-level control but lack an internal signal for identifying when a subtask has finished, making them brittle in sequential settings. We propose SeqVLA, a completion-aware extension of $π_0$ that augments the base architect…

3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
Nikolaos Gkanatsios, Jiahe Xu, Matthew Bronars, Arsalan Mousavian, Tsung-Wei Ke, Katerina Fragkiadaki
https://arxiv.org/abs/2508.11002

3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
We present 3D FlowMatch Actor (3DFA), a 3D policy architecture for robot manipulation that combines flow matching for trajectory prediction with 3D pretrained visual scene representations for learning from demonstration. 3DFA leverages 3D relative attention between action and visual tokens during action denoising, building on prior work in 3D diffusion-based single-arm policy learning. Through a combination of flow matching and targeted system-level and architectural optimizations, 3DFA achieve…

Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Benjamin Walt, Jordan Westphal, Girish Krishnan
https://arxiv.org/abs/2508.11588 htt…

Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Effective and efficient agricultural manipulation and harvesting depend on accurately understanding the current state of the grasp. The agricultural environment presents unique challenges due to its complexity, clutter, and occlusion. Additionally, fruit is physically attached to the plant, requiring precise separation during harvesting. Selecting appropriate sensors and modeling techniques is critical for obtaining reliable feedback and correctly identifying grasp states. This work investigate…