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arXiv · 2107.02464

Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation

Lars Berscheid·Pascal Meißner·Torsten Kröger
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Abstract

Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to compensate the removed time dependency, we additionally learn an image-to-image transition model that is able to predict a next state including its uncertainty. We apply this approach to bin picking, the task of emptying a bin using grasping as well as pre-grasping manipulation as fast as possible. The transition model is trained with up to 42000 pairs of real-world images before and after a manipulation action. Our approach enables two important skills: First, for applications with flange-mounted cameras, picks per hours (PPH) can be increased by around 15% by skipping image measurements. Second, we use the model to plan action sequences ahead of time and optimize time-dependent rewards, e.g. to minimize the number of actions required to empty the bin. We evaluate both improvements with real-robot experiments and achieve over 700 PPH in the YCB Box and Blocks Test.

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Lars Berscheid, Pascal Meißner, Torsten Kröger. 2021-07-06. Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation. https://arxiv.org/abs/2107.02464

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