  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%.
