100 citations · 100 across the 7 of their papers we have counts for
3 papers · 1 filter
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya +1
Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the dataset. Yet not all states are cre…
OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport
Guillaume Besset, Erwann Carn, Timothée Carecchio +5
Transferring human motion to humanoid robots requires adapting the demonstrated motion to the robot morphology while preserving interactions with the environment. This is particula…
Infeasible optimization problems and the hierarchical augmented Lagrangian method in imitation learning
Roland Andrews, Justin Carpentier, Ajay Sathya
Imitation learning (IL) is an effective approach to train complex robotics policies. Recent works have introduced hard constraints into imitation-learning optimization problems to…