6 papers · 1 filter
Training in Deployment: Human-in-the-loop Online Post-Training for Robot Manipulation
Chun Gu, Xiaofei Wei, Yiming Chen +4
Large pretrained robot foundation models provide strong priors for manipulation, yet adapting them to target tasks with offline teleoperated demonstrations often fails to cover the…
PRICE the Action Chunks: Physical Relational Credit Assignment for Embodied Reinforcement Learning
Yangang Zou, Jiajun Lu, Weitao Zhou +6
Outcome-based reinforcement learning (RL) post-trains vision--language--action policies using terminal success signals, but assigns the same trajectory-level advantage to every act…
From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
Yajie Li, Bozhou Zhang, Chun Gu +5
Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively exploiting these imagined fut…
Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining
Wenyao Zhang, Bozhou Zhang, Zekun Qi +3
Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma-misalignment of 2D image forecasting and 3D action prediction…
Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Bozhou Zhang, Nan Song, Bingzhao Gao +1
Trajectory prediction and planning in autonomous driving are highly challenging due to the complexity of predicting surrounding agents' movements and planning the ego agent's actio…
Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
Bozhou Zhang, Nan Song, Xin Jin +1
End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention. Current methods aggregate histo…