4 papers
Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration
Yang Zhang, Shixin Yang, Chenjia Bai +4
Grounding the reasoning ability of large language models (LLMs) for embodied tasks is challenging due to the complexity of the physical world. Especially, LLM planning for multi-ag…
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
Chenyou Fan, Chenjia Bai, Zhao Shan +3
Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks. However, existing multi-task planners or policies typically rely on task-specific dem…
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction
Chenyou Fan, Fangzheng Yan, Chenjia Bai +4
Learning a generalizable bimanual manipulation policy is extremely challenging for embodied agents due to the large action space and the need for coordinated arm movements. Existin…
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Xiaoyu Wen, Xudong Yu, Rui Yang +3
To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficienc…