Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation
arXiv:2107.02464
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.
arXiv comment2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
References in corpus (8)
- Toward Multimodal Image-to-Image Translation
- Data-Driven Grasp Synthesis - A Survey
- QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
- Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols
- Model-Based Reinforcement Learning for Atari
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
- End-to-End Learning of Semantic Grasping