Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenari...
Zehao Wang, Jian-Peng Yao, Yu-Ping Wang et al.· 1 citation
Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable, and a controlled formulation and taxonomy of observable preferences in simulation is proposed, covering motion, skill, and user-profile choices.
Zhi-Xu Li, Ke Tang, Li-Tian Gong et al.· 0 citations
Learning how actions change scene geometry can provide complementary supervision for goal-directed manipulation. We introduce Spatial Language Modeling, which represents scene contours, goals, action targets, and future states with a shared vocabulary of discrete coordinates and semantic tokens. A task-specific grammar...
We present DemoBridge, an toolkit that turns a single-view RGB stereo recording of a human hand demonstration into an executable, physics-validated robot-arm trajectory. Retargeting across the embodiment gap is hard. A robot arm reaches a target with a long, articulated body whose links carry far more collision volume...
Zehao Wang, Fabien Despinoy, Sergey Zakharov et al.· 0 citations
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
Yu-Ping Wang, Shuo Xing, C. Cui et al.· ACM Computing Surveys· 60 citations· ⚡2
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