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cs.RO2026
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
Minxing Li, Minghao Han, Weizhi Zhao +10
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem…
cs.RO2026
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
Bing Zhan, Shuyao Shang, Shuo Lu +6
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of…
cs.RO2025
DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving
Shuyao Shang, Yuntao Chen, Yuqi Wang +2
End-to-end autonomous driving has substantially progressed by directly predicting future trajectories from raw perception inputs, which bypasses traditional modular pipelines. Howe…