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TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Dong Jing, Jingchen Nie, Tianqi Zhang +4
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Langua…
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
Yu Fang, Wanxi Dong, Jiaqi Liu +7
Reinforcement learning holds great promise for improving robot policies beyond the limits of imitation learning. However, its practical adoption remains bottlenecked by the lack of…
REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
Puzhen Yuan, Angyuan Ma, Yunchao Yao +3
Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the enviro…
GRAPE: Generalizing Robot Policy via Preference Alignment
Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen +7
Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen task…
Embodiment-Agnostic Action Planning via Object-Part Scene Flow
Weiliang Tang, Jia-Hui Pan, Wei Zhan +6
Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the…
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Yiheng Li, Cunxin Fan, Chongjian Ge +9
Language models uncover unprecedented abilities in analyzing driving scenarios, owing to their limitless knowledge accumulated from text-based pre-training. Naturally, they should…