Planar Robot Casting with Real2Sim2Real Self-Supervised Learning
This work addresses cable management tasks in homes, warehouses, and factories, presenting an incremental improvement through a novel framework for policy learning.
The paper tackles the problem of Planar Robot Casting (PRC), where a robot arm uses a cable to reach targets beyond its workspace, by proposing Real2Sim2Real, a self-supervised learning framework that combines real and simulated data to learn policies, achieving median error distances of 8% to 14% of cable length in physical trials.
This paper introduces the task of {\em Planar Robot Casting (PRC)}: where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane toward a desired target. PRC allows the cable to reach points beyond the robot workspace and has applications for cable management in homes, warehouses, and factories. To efficiently learn a PRC policy for a given cable, we propose Real2Sim2Real, a self-supervised framework that automatically collects physical trajectory examples to tune parameters of a dynamics simulator using Differential Evolution, generates many simulated examples, and then learns a policy using a weighted combination of simulated and physical data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented, Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes and Neural Networks (NNs), and three cables with differing stiffness, torsion, and friction. Results with 240 physical trials suggest that the PRC policies can attain median error distance (as % of cable length) ranging from 8% to 14%, outperforming baselines and policies trained on only real or only simulated examples. Code, data, and videos are available at https://tinyurl.com/robotcast.