A one-shot LfD framework abstracts a single demonstration into environmental-constraint primitives, then uses self-exploration, human corrections, and compliant recovery to produce a policy that generalizes across poses and geometries, achieving over 90% success on seven real-world multi-stage tasks
Interactive imitation learning in robotics: A survey,
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CubeDAgger upgrades EnsembleDAgger with threshold regularization, optimal consensus switching, and colored noise injection to enable stable interactive imitation learning in dynamic systems, validated in simulation and real-robot scooping with 30 minutes of expert interaction.
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From a Single Demonstration to a General Policy for Contact-Rich Manipulation
A one-shot LfD framework abstracts a single demonstration into environmental-constraint primitives, then uses self-exploration, human corrections, and compliant recovery to produce a policy that generalizes across poses and geometries, achieving over 90% success on seven real-world multi-stage tasks
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CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction
CubeDAgger upgrades EnsembleDAgger with threshold regularization, optimal consensus switching, and colored noise injection to enable stable interactive imitation learning in dynamic systems, validated in simulation and real-robot scooping with 30 minutes of expert interaction.