Recent years have seen a rise in techniques for modeling discontinuous dynamics, such as
rigid contact or switching motion modes, using deep learning. A common claim is that deep …

Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …

Machine learning techniques have enabled robots to learn narrow, yet complex tasks and
also perform broad, yet simple skills with a wide variety of objects. However, learning a …

A realistic simulation environment is an essential tool in every roboticist's toolkit, with uses
ranging from planning and control to training policies with reinforcement learning. Despite …

This paper presents a reactive controller for planar manipulation tasks that leverages
machine learning to achieve real-time performance. The approach is based on a Model …

This paper addresses the identification of the inertial parameters and the contact forces
associated with objects making and breaking frictional contact with the environment. Our …

The ability to reason about and predict the outcome of contacts is paramount to the
successful execution of many robot tasks. Analytical rigid-body contact models are used …

In this paper we demonstrate the limitations of common rigid-body contact models used in
the robotics community by comparing them to a collection of data-driven and data-reinforced …

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 …

Accurately simulating real world object dynamics is essential for various applications such
as robotics, engineering, graphics, and design. To better capture complex real dynamics …