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A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
Shaopeng Zhai, Qi Zhang, Tianyi Zhang +7
Robotic real-world reinforcement learning (RL) with vision-language-action (VLA) models is bottlenecked by sparse, handcrafted rewards and inefficient exploration. We introduce VLA…
BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning
Yunpeng Qing, Yixiao Chi, Shuo Chen +5
Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets. Howe…
SMAP: Self-supervised Motion Adaptation for Physically Plausible Humanoid Whole-body Control
Haoyu Zhao, Sixu Lin, Qingwei Ben +5
This paper presents a novel framework that enables real-world humanoid robots to maintain stability while performing human-like motion. Current methods train a policy which allows…
SignBot: Learning Human-to-Humanoid Sign Language Interaction
Guanren Qiao, Sixu Lin, Ronglai Zuo +3
Sign language is a natural and visual form of language that uses movements and expressions to convey meaning, serving as a crucial means of communication for individuals who are de…
HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
Sixu Lin, Guanren Qiao, Yunxin Tai +3
Humanoid robots, capable of assuming human roles in various workplaces, have become essential to embodied intelligence. However, as robots with complex physical structures, learnin…