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Found 4 papers, 4 papers with code
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.
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.
Adaptation through prediction: multisensory active inference torque control
Adaptation to external and internal changes is major for robotic systems in uncertain environments.
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions.
