WHOLE-MoMa improves whole-body mobile manipulation by applying offline RL with Q-chunking to demonstrations from randomized sub-optimal controllers, outperforming baselines and transferring to real robots without teleoperation or real-world training data.
Perceptive model predictive control for con- tinuous mobile manipulation,
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Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers
WHOLE-MoMa improves whole-body mobile manipulation by applying offline RL with Q-chunking to demonstrations from randomized sub-optimal controllers, outperforming baselines and transferring to real robots without teleoperation or real-world training data.