Tootfinder

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
Cheonsu Jeong, Seunghyun Lee, Sunny Jeong, Sungsu Kim
https://arxiv.org/abs/2509.00398 ht…

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
This study provides an in_depth analysis of the ethical and trustworthiness challenges emerging alongside the rapid advancement of generative artificial intelligence (AI) technologies and proposes a comprehensive framework for their systematic evaluation. While generative AI, such as ChatGPT, demonstrates remarkable innovative potential, it simultaneously raises ethical and social concerns, including bias, harmfulness, copyright infringement, privacy violations, and hallucination. Current AI ev…

PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control
Haozhuo Zhang, Michele Caprio, Jing Shao, Qiang Zhang, Jian Tang, Shanghang Zhang, Wei Pan
https://arxiv.org/abs/2509.24591

PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control
We present PoseDiff, a conditional diffusion model that unifies robot state estimation and control within a single framework. At its core, PoseDiff maps raw visual observations into structured robot states-such as 3D keypoints or joint angles-from a single RGB image, eliminating the need for multi-stage pipelines or auxiliary modalities. Building upon this foundation, PoseDiff extends naturally to video-to-action inverse dynamics: by conditioning on sparse video keyframes generated by world mod…

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Zhijin Wang, Senzhen Wu, Yue Hu, Xiufeng Liu
https://arxiv.org/abs/2508.18891 http…

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data. We introduce pyFAST, a research-oriented PyTorch framework that explicitly decouples data processing from model computation, fostering a cleaner separation of concerns and facilitating rapid experimentation. Its data engine is engineered for complex scenarios…

Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow, Zeyu Shangguan, Tianheng Shi, Rong Xue, Yuxi Zheng, Yijia Weng, Yang You, Daniel Seita, Leonidas Guibas, Sergey Zakharov, Vitor Guizilini, Yue Wang
https://arxiv.org/abs/2509.22970

Robot Learning from Any Images
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a single image without requiring additional hardware or digital assets. Our framework democratizes robotic data generation by producing massive visuomotor robotic demonstrations within minutes from a wide range of image sources, including camera captures, robotic datasets, and Internet images. At its core, our approach …

Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework
Reza Akhavian, Mani Amani, Johannes Mootz, Robert Ashe, Behrad Beheshti
https://arxiv.org/abs/2509.20705

Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework
The adoption of cyber-physical systems and jobsite intelligence that connects design models, real-time site sensing, and autonomous field operations can dramatically enhance digital management in the construction industry. This paper introduces BIM2RDT (Building Information Models to Robot-Ready Site Digital Twins), an agentic artificial intelligence (AI) framework designed to transform static Building Information Modeling (BIM) into dynamic, robot-ready digital twins (DTs) that prioritize safe…

STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response
Shenghai Yuan, Weixiang Guo, Tianxin Hu, Yu Yang, Jinyu Chen, Rui Qian, Zhongyuan Liu, Lihua Xie
https://arxiv.org/abs/2509.15507

STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response
In emergency response missions, first responders must navigate cluttered indoor environments where occlusions block direct line-of-sight, concealing both life-threatening hazards and victims in need of rescue. We present STARC, a see-through AR framework for human-robot collaboration that fuses mobile-robot mapping with responder-mounted LiDAR sensing. A ground robot running LiDAR-inertial odometry performs large-area exploration and 3D human detection, while helmet- or handheld-mounted LiDAR o…

VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models
Geonung Kim, Janghyeok Han, Sunghyun Cho
https://arxiv.org/abs/2509.17985 https://

VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models
In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling flexible design exploration and rapid production of deliverables. A straightforward approach to synthesizing a video from coarse geometry might condition a video diffusion model on geometric structure. However, existing video diffusion models struggle to generate …

Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale
Tobias J\"ulg, Pierre Krack, Seongjin Bien, Yannik Blei, Khaled Gamal, Ken Nakahara, Johannes Hechtl, Roberto Calandra, Wolfram Burgard, Florian Walter
https://arxiv.org/abs/2509.14932

Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale
Vision-Language-Action models (VLAs) mark a major shift in robot learning. They replace specialized architectures and task-tailored components of expert policies with large-scale data collection and setup-specific fine-tuning. In this machine learning-focused workflow that is centered around models and scalable training, traditional robotics software frameworks become a bottleneck, while robot simulations offer only limited support for transitioning from and to real-world experiments. In this w…

Empowering Multi-Robot Cooperation via Sequential World Models
Zijie Zhao, Honglei Guo, Shengqian Chen, Kaixuan Xu, Bo Jiang, Yuanheng Zhu, Dongbin Zhao
https://arxiv.org/abs/2509.13095

Empowering Multi-Robot Cooperation via Sequential World Models
Model-based reinforcement learning (MBRL) has shown significant potential in robotics due to its high sample efficiency and planning capability. However, extending MBRL to multi-robot cooperation remains challenging due to the complexity of joint dynamics. To address this, we propose the Sequential World Model (SeqWM), a novel framework that integrates the sequential paradigm into model-based multi-agent reinforcement learning. SeqWM employs independent, sequentially structured agent-wise world…

HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms
Bingjie Chen, Zihan Wang, Zhe Han, Guoping Pan, Yi Cheng, Houde Liu
https://arxiv.org/abs/2509.20263

HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms
Traditional IK methods for redundant humanoid manipulators emphasize end-effector (EE) tracking, frequently producing configurations that are valid mechanically but not human-like. We present Human-Like Inverse Kinematics (HL-IK), a lightweight IK framework that preserves EE tracking while shaping whole-arm configurations to appear human-like, without full-body sensing at runtime. The key idea is a learned elbow prior: using large-scale human motion data retargeted to the robot, we train a FiLM…