Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation
Adrian S. Bauer,Peter Schmaus,Freek Stulp,Daniel Leidner,Adrian S. Bauer,Peter Schmaus,Freek Stulp,Daniel Leidner
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 a...
