9 papers
Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation
Zeyi Liu, Guangyao Liu, Yinuo Qu +6
Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may incl…
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
Haichao Liu, Yuyao Jiang, Hyunsun Park +2
Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Traditional teleoperation systems often fail in contact-rich tasks because embodiment ga…
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
Zhenyu Wu, Xiuwei Xu, Yukun Zhou +8
Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely o…
Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
Hongye Cao, Nuo Yan, Haoyuan Deng +5
Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating exter…
SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM
Chuanrui Zhang, Minghan Qin, Yuang Wang +3
High-quality articulated 3D assets are indispensable for embodied AI and physical simulation, yet 3D generation still focuses on static meshes, leaving a gap in "sim-ready" interac…
UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph
Haichao Liu, Yuanjiang Xue, Yuheng Zhou +4
Achieving general-purpose robotic manipulation requires robots to seamlessly bridge high-level semantic intent with low-level physical interaction in unstructured environments. How…