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Motorcycles can navigate complex road hazards more safely thanks to a new system that uses VLMs to understand and map risks in a way that's tailored to their unique dynamics.
Unlock 80% faster autonomous parking by learning a single intermediate pose that simplifies complex maneuvers.
LLMs can be used to prune irrelevant information *before* planning, enabling efficient long-horizon multi-robot task planning that outperforms both pure LLM and hybrid LLM-PDDL approaches.
Ditching short-term motion prediction for explicit intention prediction from motion history unlocks more accurate and socially acceptable autonomous valet parking.