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.
|Publication status||Accepted/In press - 6 Jul 2021|
|Event||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Online, Prague|
Duration: 27 Sep 2021 → 1 Oct 2021
|Conference||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Abbreviated title||IROS 2021|
|Period||27/09/21 → 1/10/21|