Iterative learning of grasp adaptation through human corrections

Eric L. Sauser*, Brenna D. Argall, Giorgio Metta, Aude G. Billard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work, we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posturecontact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correctionreplay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.

Original languageEnglish (US)
Pages (from-to)55-71
Number of pages17
JournalRobotics and Autonomous Systems
Volume60
Issue number1
DOIs
StatePublished - Jan 2012

Funding

The research leading to these results has received funding from the Swiss National Science Foundation through the NCRR in Robotics, and the European Community’s Seventh Framework Programme FP7/2007-2013–Challenge 2–Cognitive Systems, Interaction, Robotics — under grant agreement no. [231500]-[ROBOSKIN] .

Keywords

  • Grasp adaptation
  • Humanoid robots
  • Learning and adaptive systems
  • Policy refinement
  • Tactile sensing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • General Mathematics
  • Computer Science Applications

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