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
A unified approach for motion and force control of robot manipulators: The operational space formulation,
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An intent-driven Real2Sim framework uses VLMs for semantic task decomposition to identify missing physical parameters and generates reactive behavior trees to acquire them via contact-rich robotic interactions on a Franka Panda arm.
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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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Real2Sim via Active Perception with Behavior Trees Automatically Generated by VLMs
An intent-driven Real2Sim framework uses VLMs for semantic task decomposition to identify missing physical parameters and generates reactive behavior trees to acquire them via contact-rich robotic interactions on a Franka Panda arm.