Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
The Visual Computer: International Journal of Computer Graphics - Special section on implicit surfaces
Learning from observation using primitives
Learning from observation using primitives
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Active learning with statistical models
Journal of Artificial Intelligence Research
Learning motor primitives for robotics
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning mobile robot motion control from demonstration and corrective feedback
Learning mobile robot motion control from demonstration and corrective feedback
Iterative learning of grasp adaptation through human corrections
Robotics and Autonomous Systems
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Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations, for example correspondence issues between the robot and demonstrator, can degrade policy performance. This work presents an approach for policy improvement through a tactile interface located on the body of the robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies. The TPC algorithm is validated on humanoid robot performing grasp positioning tasks. The performance of the demonstrated policy is found to improve with tactile corrections. Tactile guidance also is shown to enable the development of policies able to successfully execute novel, undemonstrated, tasks. We further show that different modalities, namely teleoperation and tactile control, provide information about allowable variability in the target behavior in different areas of the state space.