12 papers
Praxis: Distilling Physical Interaction Priors from Egocentric Videos for Generalizable Whole-Body Manipulation
Shuliang He, Ruiyan Xu, Bo Yue +5
Mobile humanoid manipulation requires both reaching a usable workspace and preserving precise hand-object interactions as object poses and contact conditions change. Learning these…
TaskAnchor: Grounding Task State in Reactive VLAs for Long-Horizon Manipulation
Hengyan Liu, Wenlve Zhou, Bo Yue +7
Reactive vision-language-action (VLA) policies suffer from task-state aliasing in long-horizon manipulation, where identical multimodal inputs call for distinct, context-dependent…
RoboReact: Agentic Skill Distillation from Generated Egocentric Videos for Generalizable Whole-Body Manipulation
Shuliang He, Shuai Wang, Bo Yue +3
Humanoid robots have the potential to perform dexterous manipulation in human environments, yet acquiring diverse and generalizable skills remains costly due to expensive hardware…
LoopVLA: cross-subtask event memory for looped task execution
Shoujing Zhu, Zhenyang Liu, Fungmiu Wang +6
Repetitive tasks are common in real-world manipulation, yet current vision-language-action (VLA) policies remain unreliable for repeated actions. We present LoopVLA, a cross-subtas…
From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
Sheng Xu, Ruixing Jin, Huayi Zhou +6
Although robotic manipulation has made significant progress, reliable execution remains challenging because task failures are inevitable in dynamic and unstructured environments. T…
Grounding Sim-to-Real Generalization in Robotic Manipulation: An Empirical Study with Vision-Language-Action Models
Ruixing Jin, Zicheng Zhu, Ruixiang Ouyang +4
Learning a generalist control policy for robotic manipulation typically relies on large-scale datasets. Given the high cost of real-world data collection, a practical alternative i…
MagicAgent: Towards Generalized Agent Planning
Xuhui Ren, Shaokang Dong, Chen Yang +21
The evolution of Large Language Models (LLMs) from passive text processors to autonomous agents has established planning as a core component of modern intelligence. However, achiev…
Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery
Bo Yue, Sheng Xu, Kui Jia +1
Humanoid robots represent a central frontier in embodied intelligence, as their anthropomorphic form enables natural deployment in humans' workspace. Brain-body co-design for human…
Toward Physical Recursive Self-Improvement: A Call for Body-Control Co-Design for Humanoid Robot
Guiliang Liu, Bo Yue, Yi Jin Kim +1
Humanoid robots, as general-purpose physical agents, must integrate both intelligent control and adaptive morphology to operate effectively in diverse real-world environments. Whil…
Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop
Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…
Real-Time Verification of Embodied Reasoning for Generative Skill Acquisition
Bo Yue, Shuqi Guo, Kaiyu Hu +4
Generative skill acquisition enables embodied agents to actively learn a scalable and evolving repertoire of control skills, crucial for the advancement of large decision models. W…
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
Bo Yue, Jian Li, Guiliang Liu
Optimizing objective functions subject to constraints is fundamental in many real-world applications. However, these constraints are often not readily defined and must be inferred…