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Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Yao Feng, Chendong Xiang, Xinyi Mao +7
Robotic arm manipulation in data-scarce settings is a highly challenging task due to the complex embodiment dynamics and diverse contexts. Recent video-based approaches have shown…
Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models
Yuhua Jiang, Jiawei Huang, Yufeng Yuan +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing Large Language Models (LLMs) on complex reasoning tasks. However, existing methods suffer f…
Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online
Yishu Li, Xinyi Mao, Ying Yuan +3
We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous object…
Vidar: Embodied Video Diffusion Model for Generalist Manipulation
Yao Feng, Hengkai Tan, Xinyi Mao +5
Scaling general-purpose manipulation to new robot embodiments remains challenging: each platform typically needs large, homogeneous demonstrations, and end-to-end pixel-to-action p…
AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
Hengkai Tan, Yao Feng, Xinyi Mao +5
Learning generalizable manipulation policies hinges on data, yet robot manipulation data is scarce and often entangled with specific embodiments, making both cross-task and cross-p…