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
Dynamic movement primitives in robotics: A tutorial survey
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Incremental Iterative Reference Learning Control refines accelerated demonstrations to achieve up to 10x faster execution in contact-rich imitation learning with 22.5% better trajectory similarity than direct IRLC and improved policy success.
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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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Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Incremental Iterative Reference Learning Control refines accelerated demonstrations to achieve up to 10x faster execution in contact-rich imitation learning with 22.5% better trajectory similarity than direct IRLC and improved policy success.