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 …

Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that
quickly adapt to many tasks with varying rewards or dynamics functions. However, learned …

Frictional contact has been extensively studied as the core underlying behavior of legged
locomotion and manipulation, and its nearly-discontinuous nature makes planning and …

Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand
challenge for existing rigid-body physics simulators. This paper introduces a data …

Robotic systems, such as legged robots and manipulators, often handle states which involve
ground impact or interaction with objects present in their surroundings; both of which are …

Imperfect, useful dynamical models have enabled significant progress in planning and
controlling robotic locomotion and manipulation. Traditionally, these models have been …

As robots are increasingly deployed in contact-rich tasks, there has been increased interest
in models of contact that are more accurate than those of untuned simulations. These …

A majority of microrobots are constructed using compliant materials that are difficult to model
analytically, limiting the utility of traditional model-based controllers. Challenges in data …

In this paper, we evaluate the prediction performance of two nonsmooth rigid-body dynamics
simulators on realworld data with spatial impacts in the context of robotic tossing and visual …

With an additional boost due to the Covid-19 pandemic, the logistics market is growing faster
than ever before and the potential workforce cannot keep up with demands from industry …