7 papers
PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
Xianghui Wang, Feng Chen, Wenbo Zhang +4
Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execution efficiency. While existi…
FlashNav: Ultra-Fast Policy Training for Robot Navigation within 20 Seconds
Shanze Wang, Yiwei Qian, Xinming Zhang +6
Deep reinforcement learning has shown strong potential for robot navigation, but its practical deployment is still limited by the long wall-clock cost of policy training. This pape…
Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
Shanze Wang, Xinming Zhang, Siwei Cheng +4
Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation, this remains the standard p…
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Xianghui Wang, Siwei Cheng, Shanze Wang +3
Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of successful behaviors and prov…
MA-ROESL: Motion-aware Rapid Reward Optimization for Efficient Robot Skill Learning from Single Videos
Xianghui Wang, Xinming Zhang, Yanjun Chen +2
Vision-language models (VLMs) have demonstrated excellent high-level planning capabilities, enabling locomotion skill learning from video demonstrations without the need for meticu…
Rethinking Soft Actor-Critic in High-Dimensional Action Spaces: The Cost of Ignoring Distribution Shift
Yanjun Chen, Xinming Zhang, Xianghui Wang +3
Soft Actor-Critic algorithm is widely recognized for its robust performance across a range of deep reinforcement learning tasks, where it leverages the tanh transformation to const…