Work

Towards Imitation Learning of Dynamic Manipulation Tasks: A Framework to Learn from Failures

Year: 2014

Type: article

Abstract: We present an imitation learning approach for a dynamic fluid pouring task.Our approach allows learning from errors made by humans and how they recovered from these errors subsequently.We collect both successful and failed human demonstrations of the task.Our algorithm combines a support vector machine based classifier and iterative search to generate initial task parameters for the robot.Next, a refinement algorithm, capturing how demonstrators change parameters to transition from failure to success, enables the robot to address failures.Experimental results with a physical robot are reported to illustrate our approach.

Source: University Libraries (University of Maryland)

Authors: Joshua D. Langsfeld, Krishnanand N. Kaipa, Rodolphe J. Gentili, James A. Reggia, Satyandra K. Gupta

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Office of Naval Research

Award: G1802050954

OpenAlex ID: W92783539

FWCI: 0.743

Cites: 26

Cited by: 4

Related to: 10