Learning and generalization of motor skills by learning from demonstration

Citations Over TimeTop 1% of 2009 papers

Abstract

We provide a general approach for learning robotic motor skills from human demonstration. To represent an observed movement, a non-linear differential equation is learned such that it reproduces this movement. Based on this representation, we build a library of movements by labeling each recorded movement according to task and context (e.g., grasping, placing, and releasing). Our differential equation is formulated such that generalization can be achieved simply by adapting a start and a goal parameter in the equation to the desired position values of a movement. For object manipulation, we present how our framework extends to the control of gripper orientation and finger position. The feasibility of our approach is demonstrated in simulation as well as on the Sarcos dextrous robot arm. The robot learned a pick-and-place operation and a water-serving task and could generalize these tasks to novel situations.

Related Papers

• → Introduction to the Special Issue: Toward an Explicit Technology for Generalizing Academic Behavior(2010)14 cited
• → On a Generalization of Yager’s Implications(2012)8 cited
• → Behavior therapy: Problems in Generalization(1971)19 cited
• → Nota sobre o problema da generalização(2014)
• The New Segregation and Generalization of IMO42-2(2005)