KinDER is a new open-source benchmark that demonstrates substantial gaps in current robot learning and planning methods for handling physical constraints.
Diffusion policy: Vi- suomotor policy learning via action diffusion
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Nonasymptotic analysis shows sub-Gaussian action errors in behavior cloning propagate through gain-dependent closed-loop dynamics to produce sub-Gaussian position errors whose tail is governed by a proxy matrix and amplification index that depends on controller stiffness and damping.
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KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
KinDER is a new open-source benchmark that demonstrates substantial gaps in current robot learning and planning methods for handling physical constraints.
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A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning
Nonasymptotic analysis shows sub-Gaussian action errors in behavior cloning propagate through gain-dependent closed-loop dynamics to produce sub-Gaussian position errors whose tail is governed by a proxy matrix and amplification index that depends on controller stiffness and damping.