8 papers · 1 filter
Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning
Feng Chen, Pei Zhou, Lixuan He +7
Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependen…
Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models
Shijun Shi, Zhe Liu, Zhi Hou +5
World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level instructions may leave the next…
ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring
Jingyuan Liu, Yunpeng Qing, Changxu Wei +1
Despite rapid progress, generalist robot policies remain brittle during execution. Reliable deployment therefore requires runtime monitoring that provides timely warnings of failur…
StateSkill-Agent: An Effective and Efficient Embodied Agent via State-conditioned Skill
Changxu Wei, Jingyi Zhang, Zhiyu Zheng +6
In this work, we aim to develop an embodied MLLM agent that understands and solves embodied tasks by learning, abstracting, and reusing transferrable and generalizable behavioral p…
PF-RL: Progress Field Reinforcement Learning via Goal-Conditioned Value Geometry for Vision-Language-Action Models
Yunpeng Qing, Yilun Kong, Sixu Lin +9
Reinforcement Fine-Tuning~(RFT) has emerged as a promising paradigm for improving Vision-Language-Action~(VLA) policies, yet sparse task-level outcomes provide limited credit for i…
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…