5 papers
Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents
Hongxi Yan, Ziyue Huang, Shichao Fan +1
Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existing critic-free methods propag…
XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
Shichao Fan, Kun Wu, Zhengping Che +12
Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. However, existing VLA models still f…
RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
Chengkai Hou, Kun Wu, Jiaming Liu +30
While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstration…
Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation
Shichao Fan, Quantao Yang, Yajie Liu +4
Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current im…
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
Kun Wu, Chengkai Hou, Jiaming Liu +34
In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse…