Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Using Humanoid Robots to Study Human Behavior
IEEE Intelligent Systems
Constructive Incremental Learning from Only Local Information
Neural Computation
Can co-activation reduce kinematic variability? A simulation study
Biological Cybernetics
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Multimodal telepresent control of DLR's rollin' JUSTIN
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Kinesthetic bootstrapping: teaching motor skills to humanoid robots through physical interaction
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Combining active learning and reactive control for robot grasping
Robotics and Autonomous Systems
A survey of Tactile Human-Robot Interactions
Robotics and Autonomous Systems
Human sensorimotor learning for humanoid robot skill synthesis
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Iterative learning of grasp adaptation through human corrections
Robotics and Autonomous Systems
On-line frequency adaptation and movement imitation for rhythmic robotic tasks
International Journal of Robotics Research
Robotic grasping and manipulation through human visuomotor learning
Robotics and Autonomous Systems
Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
IEEE Transactions on Robotics
Tele-impedance: Teleoperation with impedance regulation using a body-machine interface
International Journal of Robotics Research
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We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot's motion and compliance according to the partner's actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner's motion.