1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Yang Zhang, Chenjia Bai, Bin Zhao +3
Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a wo…
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
Xiaoyu Wen, Chenjia Bai, Kang Xu +4
Cross-domain offline reinforcement learning leverages source domain data with diverse transition dynamics to alleviate the data requirement for the target domain. However, simply m…
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…