Instance-Agnostic Geometry and Contact Dynamics Learning

no code implementations • 11 Sep 2023 • Mengti Sun, Bowen Jiang, Bibit Bianchini, Camillo Jose Taylor, Michael Posa

This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of geometry as a shared representation.

Learning Linear Complementarity Systems

no code implementations • 25 Dec 2021 • Wanxin Jin, Alp Aydinoglu, Mathew Halm, Michael Posa

This paper investigates the learning, or system identification, of a class of piecewise-affine dynamical systems known as linear complementarity systems (LCSs).

ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations

1 code implementation • 23 Sep 2020 • Samuel Pfrommer, Mathew Halm, Michael Posa

Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior.

Stabilization of Complementarity Systems via Contact-Aware Controllers

2 code implementations • 3 Aug 2020 • Alp Aydinoglu, Victor M. Preciado, Michael Posa

We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics.

Robotics

Contact-Aware Controller Design for Complementarity Systems

2 code implementations • 24 Sep 2019 • Alp Aydinoglu, Victor M. Preciado, Michael Posa

While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion.

Robotics