KiTe augments sampling-based kinodynamic planning with terminal costs in belief space, proving asymptotic optimality preservation and improved goal-reaching probability bounds via Wasserstein minimization, supported by learned uncertainty models and experiments.
Mujoco: A physics engine for model- based control
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Excessive sim2real focus impedes robotics policy learning via simulator lock-in; a kinematics-only sim2sim2real paradigm is proposed to restore exploration.
citing papers explorer
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Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
KiTe augments sampling-based kinodynamic planning with terminal costs in belief space, proving asymptotic optimality preservation and improved goal-reaching probability bounds via Wasserstein minimization, supported by learned uncertainty models and experiments.
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Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
Excessive sim2real focus impedes robotics policy learning via simulator lock-in; a kinematics-only sim2sim2real paradigm is proposed to restore exploration.