1 citations · 1 across the 9 of their papers we have counts for
10 papers
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
Yuyao Jiang, Haichao Liu, Jiarui Zheng +3
Tactile sensing is particularly valuable for contact-rich robotic manipulation. Recent work has made substantial progress in tactile representation learning for robotic manipulatio…
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: Open-World Robotic Manipulation via Agentic Operational Graphs
Haichao Liu, Yuanjiang Xue, Yuheng Zhou +5
Achieving general-purpose robotic manipulation requires robots to bridge high-level semantic intent with low-level physical interaction in unstructured environments. However, zero-…
SA-VLA: Spatially-Aware Flow-Matching for Vision-Language-Action Reinforcement Learning
Xu Pan, Zhenglin Wan, Xingrui Yu +6
Vision-Language-Action (VLA) models exhibit strong generalization in robotic manipulation, yet reinforcement learning (RL) fine-tuning often degrades robustness under spatial distr…
RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
Zongzheng Zhang, Chenghao Yue, Haobo Xu +5
Robotic chemists promise to both liberate human experts from repetitive tasks and accelerate scientific discovery, yet remain in their infancy. Chemical experiments involve long-ho…
TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
Zongzheng Zhang, Haobo Xu, Zhuo Yang +5
Many robotic manipulation tasks require sensing and responding to force signals such as torque to assess whether the task has been successfully completed and to enable closed-loop…