PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gambit: An Autonomous Chess-Playing Robotic System
Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc Deisenroth, Liefeng Bo, Robert Chu, Mike Kung, Louis LeGrand, Joshua Smith and Dieter Fox
In: ICRA 2011, May 2011, Shanghai, China.

Abstract

This paper presents Gambit, a custom, mid-cost 6-DoF robot manipulator system that can play physical board games against human opponents in non-idealized environments. Historically, unconstrained robotic manipulation in board games has often proven to be more challenging than the underlying game reasoning, making it an ideal testbed for small-scale manipulation. The Gambit system includes a low-cost Kinect-style visual sensor, a custom manipulator, and state-of-the-art learning algorithms for automatic detection and recognition of the board and objects on it. As a use-case, we describe playing chess quickly and accurately with arbitrary, uninstrumented boards and pieces, demonstrating that Gambit's engineering and design represent a new state-of-the-art in fast, robust tabletop manipulation.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:7627
Deposited By:Marc Deisenroth
Deposited On:17 March 2011