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vision-driven-robotic-manipulation

topic1 events
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.

robotics/simulation/inference/manipulation