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Found 3 papers, 2 papers with code
Adaptation through prediction: multisensory active inference torque control
Adaptation to external and internal changes is major for robotic systems in uncertain environments.
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections.
ILoSA: Interactive Learning of Stiffness and Attractors
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives.
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