Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation
This work addresses the problem of efficient robotic manipulation for industrial applications like bin picking, offering incremental improvements in speed and planning.
The paper tackles the challenge of time-consuming robot learning for manipulation tasks by learning a generative transition model that predicts next states with uncertainty, enabling a 15% increase in picks per hour and achieving over 700 PPH in real-robot experiments.
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.