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papersSEP 10 04:00 UTC

Probabilistic Real2Sim2Real approach improves vision-driven deformable linear object manipulation

A new research paper applies likelihood-free inference to real2sim2real transfer for manipulating deformable linear objects such as cables using vision. By estimating a distribution over simulation parameters from black-box models, the method handles nonlinear and stochastic dynamics that are hard to model directly. A posterior-driven heuristic then adapts the inferred parameter support so control policies can generalize to varied deployment conditions.

deformable-linear-object-manipulationlikelihood-free inferencereal2sim2realrobot-control-policiessim-to-real transfervision-driven-robotic-manipulation

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGPosterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation ↗SEP 10 04:00 UTC

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