Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
Nima Fazeli,Anurag Ajay,Alberto Rodriguez,Nima Fazeli,Anurag Ajay,Alberto Rodriguez
The ability to simulate and predict the outcome of contacts is paramount to the successful execution of many robotic tasks. Simulators are powerful tools for the design of robots and their behaviors, yet the discrepancy between their predictions and observed data limit their usability. In this paper, we propose a self-supervised approach to learning residual models for rigid-body simulators that e...
