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
Multistage cable routing through hierarchical imitation learning,
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Hierarchical framework pairs in-context VLMs for high-level plan synthesis with RL-trained low-level skills and failure recovery to reach 92% success on long-horizon DLO routing across varied scenes and language inputs.
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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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Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models
Hierarchical framework pairs in-context VLMs for high-level plan synthesis with RL-trained low-level skills and failure recovery to reach 92% success on long-horizon DLO routing across varied scenes and language inputs.