7 papers
TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement
Zhixuan Zhao, Peiyan Li, Enhao Zhang +10
DAgger improves robot policies by aggregating expert supervision from states visited during policy execution. Robot-gated DAgger automates expert queries, allowing the robot to dec…
Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning
Peiyan Li, Yueran Tao, Enhao Zhang +10
Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may refle…
TPO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
Haixin Wang, Hejie Cui, Chenwei Zhang +7
Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization te…
Diff-Muscle: Efficient Learning for Musculoskeletal Robotic Table Tennis
Wentao Zhao, Jun Guo, Kangyao Huang +2
Musculoskeletal robots provide superior advantages in flexibility and dexterity, positioning them as a promising frontier towards embodied intelligence. However, current research i…
Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning
Peihao Wang, Shan Yang, Xijun Wang +8
Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal-directed actions, a capability…
Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities
Yongshuai Liu, Taeyeong Choi, Xin Liu
Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific,…
Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning
Yongshuai Liu, Xin Liu
Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate mode…