Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
IEEE Transactions on Robotics2008Vol. 24(6), pp. 1463–1467
Citations Over TimeTop 1% of 2008 papers
Abstract
We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment. It combines a dynamical system control approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator. The system is validated on two experiments involving a humanoid robot: putting an object into a box and reaching for and grasping an object.
Related Papers
- → A deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots(2017)58 cited
- → Analytical inverse kinematic solution using the D-H method for a 6-DOF robot(2017)46 cited
- Kinesthetic learning of behaviors in a humanoid robot(2011)
- → Kinematics Modeling and Analysis of a Novel Five-DoF Spraying Robot(2019)1 cited
- → A Deep Reinforcement Learning Approach for Dynamically Stable Inverse Kinematics of Humanoid Robots(2018)6 cited