Project: Data-Driven and Data-Augmented Contact Models
Experience says that the models of frictional contact that we use in robotics are noisy. Robots benefit from models that are computationally efficient, accurate, and thoroughly evaluated. Our work focuses evaluating and reinforcing contact models for frictional and impulsive forces. We want to understand when we can trust contact models derived from constitutive laws like Coulomb friction or principle of Maximal Dissipation. We are also interested in using data to reinforce these models and use them in perception, planning and control.
Contact Datasets. We have developed techniques to automate and instrument experiments involving frictional dynamics in a robot arena with precise robot manipulators and carefully calibrated motion tracking and force sensing. This has lead to open datasets of controlled experiments to learn models of frictional sliding and impact.
Data-reinforced models of frictional dynamics.We have shown that experimental data can be used to learn or reinforce models that outperform constitutive laws of friction and restitution, and to capture their inherent variability. We have explored this approach on the dynamics of planar pushing and planar rigid impacts.
Stochastic simulation and planning with data-reinforced models. We are exploring a dynamic filtering scheme GP-SUM, that exploits the algebraic structure of Gaussian Processes to efficiently propagate non-Gaussian beliefs, to simulate state distributions through data-reinforced contact models.
2020 ICRA "Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners", N. Fazeli, A. Ajay and A. Rodriguez. [Bibtex]
2020 ICRA "Accurate Vision-based Manipulation through Contact Reasoning", A. Kloss, M. Bauza, J. Wu, J. Tenenbaum, A. Rodriguez and J. Bohg. [Bibtex]
2019 IROS "Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGBD images", M. Bauza, F. Alet, Y. Lin, T. Lozano-Perez, L. Kaelbling, P. Isola and A. Rodriguez. [Bibtex]
2018 WAFR "GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs", M. Bauza and A. Rodriguez. [Bibtex]
2018 IROS "Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing", A. Ajay, J. Wu, N. Fazeli, M. Bauza, L. Kaelbling, J. Tenebaum and A. Rodriguez. [Bibtex] Best Cognitive Paper
2018 RA-L "Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-Collection Bias", D. Ma and A. Rodriguez. [Bibtex]
2017 ISRR "Fundamental Limitations in Performance and Interpretability of Common Planar Rigid-Body Contact Models", N. Fazeli, S. Zapolsky, E. Drumwright, and A. Rodriguez. [Bibtex]
2017 CoRL "Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact", N. Fazeli, S. Zapolsky, E. Drumwright, and A. Rodriguez. [Bibtex]
2017 ICRA "A Probabilistic Data-Driven Model for Planar Pushing", M. Bauza, and A. Rodriguez. [Bibtex]
2017 ICRA "Empirical Evaluation of Common Impact Models on a Planar Impact Task", N. Fazeli, E. Donlon, E. Drumwright and A. Rodriguez. [Bibtex]
2016 IROS "More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing", KT. Yu, M. Bauza, N. Fazeli, and A. Rodriguez. [Bibtex] Best Paper Award Finalist
Related Videos
IROS 2016 - More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing Best Paper Award Finalist
