This years International Conference on Robotics and Automation (ICRA) goes online, and we are participating with a paper on Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation:
Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occur at any time. To enhance failure tolerance, we propose to predict the effects of robot actions by augmenting collected experience with semantic knowledge and leveraging realistic physics simulations. That is, we consider semantic similarity of actions in order to predict outcome probabilities for previously unknown tasks. Furthermore, physical simulation is used to gather simulated experience that makes the approach robust even in extreme cases. We show how this concept is used to predict action success probabilities and how this information can be exploited throughout future planning trials. The concept is evaluated in a series of real world experiments conducted with the humanoid robot Rollin’ Justin.
The paper by Adrian Bauer, Peter Schmaus, Freek Stulp, and Daniel Leidner will be present as digital submission during ICRA 2020, from May 31 to August 31.
Image Credit: DLR