Work

Prepare the Chair for the Bear! Robot Imagination of Sitting Affordance to Reorient Previously Unseen Chairs

Year: 2023

Type: article

Abstract: In this letter, a paradigm for the classification and manipulation of novel objects is established and demonstrated with the example of chairs. Our approach leverages the robot's understanding of object stability, perceptibility, and affordance to prepare previously unseen and randomly oriented chairs on which a teddy bear is to be seated. The teddy bear is a proxy for an elderly person, hospital patient, or child. By autonomously reconstructing a complete model of the object and inserting it into a physical simulator (i.e., the robot's “imagination”), the robot assesses whether or not the object is a chair and, if it is, determines how to reorient it properly to be used. Experimental results show that our method achieves a high success rate on the real robot task of chair preparation. Also, it outperforms several baseline methods on the task of upright pose prediction for chairs. The same methodology can be easily transferred to a wide variety of application scenarios, and illustrates a broader paradigm in affordance-based reasoning.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2023.3306671

Authors: Xin Meng, Hongtao Wu, Sipu Ruan, Gregory S. Chirikjian

Institutions: National University of Singapore, Johns Hopkins University

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,345

Funders: Johns Hopkins University, National Research Foundation, National Research Foundation Singapore