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UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan +3
Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing int…
DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents
Haoyuan Deng, Jiebin Liu, Tengxiao Zhang +3
Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires coordination between semantic reasoning and physical execution. Semantic reason…
Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
Haoyuan Deng, Haichao Liu, Wenkai Guo +6
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundat…
UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
Haoyuan Deng, Yitong Gao, Yudong Lin +3
Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.…
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…
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-…
E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning
Haoyuan Deng, Yudong Lin, Yuanjiang Xue +5
Human-in-the-loop guidance has emerged as an effective approach for accelerating online reinforcement learning (RL) in real-world manipulation. However, existing human-in-the-loop…