6 papers
MARBLE: Multi-Aspect Reward Balance for Diffusion RL
Canyu Zhao, Hao Chen, Yunze Tong +3
Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is intrinsically a multi-dimensi…
Grounding Actions in Camera Space: Observation-Centric Vision-Language-Action Policy
Tianyi Zhang, Haonan Duan, Haoran Hao +3
Vision-Language-Action (VLA) models frequently encounter challenges in generalizing to real-world environments due to inherent discrepancies between observation and action spaces.…
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy
Zhi Hou, Tianyi Zhang, Yuwen Xiong +8
While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact ac…
Diffusion Transformer Policy
Zhi Hou, Tianyi Zhang, Yuwen Xiong +6
Recent large vision-language-action models pretrained on diverse robot datasets have demonstrated the potential for generalizing to new environments with a few in-domain data. Howe…
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation
Fuxian Huang, Qi Zhang, Shaopeng Zhai +6
With the rapid development of artificial intelligence, multimodal learning has become an important research area. For intelligent agents, the state is a crucial modality to convey…
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation
Shaopeng Zhai, Jie Wang, Tianyi Zhang +6
Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language…