Robot policies trained on human interventions that rewind to a familiar state and then correct the mistake achieve higher long-horizon success and better data efficiency than imitation on full demonstrations alone.
Areductionofimitationlearningandstructured prediction to no-regret online learning
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RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
Robot policies trained on human interventions that rewind to a familiar state and then correct the mistake achieve higher long-horizon success and better data efficiency than imitation on full demonstrations alone.