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Equal nominal success in dexterous manipulation doesn't mean your AI can handle speed variations like a human expert.
Exact Signed Distance Functions enable non-conservative navigation strategies that enhance safety recovery in complex polytopic environments.
FIRMGrasp reveals that over half of the grasps deemed high-quality by traditional metrics fail under adverse friction conditions, highlighting a critical gap in grasp assessment.
Variational neural belief parameterizations can drastically enhance grasping success rates and reduce planning time under multimodal uncertainties, outperforming traditional methods by an order of magnitude.
Achieve safe, real-time robot navigation in complex environments by combining global MILP planning with local, geometry-aware CBF control.
