8 papers
Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials
Yihang Hu, Pingyue Sheng, Yuyang Liu +2
Embodied robots have achieved strong performance in many real-world manipulation tasks, yet agile dynamic manipulation remains challenging due to high sensitivity to motion paramet…
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
Weirui Ye, Yunsheng Zhang, Haoyang Weng +6
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For on…
EasyInsert: A Data-Efficient and Generalizable Insertion Policy
Guanghe Li, Junming Zhao, Shengjie Wang +1
Robotic insertion is a highly challenging task that requires exceptional precision in cluttered environments. Existing methods often have poor generalization capabilities. They typ…
Translating Flow to Policy via Hindsight Online Imitation
Yitian Zheng, Zhangchen Ye, Weijun Dong +5
Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions. This design allows training the…
Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents
Zonghan Yang, Shengjie Wang, Kelin Fu +18
Large Language Models (LLMs) are increasingly applied to software engineering (SWE), with SWE-bench as a key benchmark. Solutions are split into SWE-Agent frameworks with multi-tur…
Do You Need Proprioceptive States in Visuomotor Policies?
Juntu Zhao, Wenbo Lu, Di Zhang +10
Imitation-learning-based visuomotor policies have been widely used in robot manipulation, where both visual observations and proprioceptive states are typically adopted together fo…