Published: Jan. 25, 2023
A complete peg-in-hole assembly sequence: A The bearing is presented in a 3Dprinted jig, B The bearing is picked up by the robot and transported to the assembly plate C. Force and torque measurements are used to D locate the hole E and complete insertion. Insertion failure due to misalignment F. Friction with the edge of the hole has caused the twisting action to pull the bearing further from the hole center.

Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful. We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. Examples include a robot trying to connect with a charging station, parallel parking, or assembling a tightly-fitting part. The ability to restart after predicting failure early, versus simply running to failure, can significantly decrease the makespan, that is, the total time to completion, with the drawback of potentially short-cutting an otherwise successful operation. Assuming task running times to be Poisson distributed, and using a Markov Jump process to capture the dynamics of the underlying Markov Decision Process, we derive a closed form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time. We demonstrate this approach using a robotic peg-in-hole assembly problem using a real robotic system. Failures are predicted by a dilated convolutional network based on force-torque data, showing an average makespan reduction from 101s to 81s (N=120, p<0.05). We posit that the proposed algorithm generalizes to any robotic behavior with an unambiguous terminal reward, with wide ranging applications on how robots can learn and improve their behaviors in the wild.


Watson, J. and Correll, N., 2023. Optimal decision making in robotic assembly and other trial-and-error tasks. Int. Conf. on Intelligent Robots and Systems (IROS), Detroit, MI.