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.
Alpha-αand bi-act are all you need: Importance of position and force information/control for imitation learning of unimanual and bimanual robotic manipulation with low-cost system
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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.