Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking

TL;DR

This paper introduces non-Markov policies with memory to reduce sequential grasp failures in robot bin picking, significantly improving success rates and efficiency over traditional Markov policies.

Contribution

It characterizes a new class of objects prone to sequential failures and proposes memory-based policies that outperform Markov policies in simulation and real-world tests.

Findings

Non-Markov policies reduce sequential failure rates.

Non-Markov policies increase mean successful picks per hour by over 100%.

Simulation and physical experiments validate the effectiveness of the proposed methods.

Abstract

A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce. To accommodate a wide variety of products, many automated systems include multiple gripper types and/or tool changers. However, for some objects, sequential grasp failures are common: when a computed grasp fails to lift and remove the object, the bin is often left unchanged; as the sensor input is consistent, the system retries the same grasp over and over, resulting in a significant reduction in mean successful picks per hour (MPPH). Based on an empirical study of sequential failures, we characterize a class of "sequential failure objects" (SFOs) -- objects prone to sequential failures based on a novel taxonomy. We then propose three non-Markov picking policies that incorporate memory of past failures to modify subsequent actions. Simulation experiments on SFO models and the EGAD dataset suggest that the non-Markov policies significantly outperform the Markov policy in terms of the sequential failure rate and MPPH. In physical experiments on 50 heaps of 12 SFOs the most effective Non-Markov policy increased MPPH over the Dex-Net Markov policy by 107%.