We investigate whether a robot arm can learn to pick and throw arbitrary rigid objects into
selected boxes quickly and accurately. Throwing has the potential to increase the physical …

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

An efficient, generalizable physical simulator with universal uncertainty estimates has wide
applications in robot state estimation, planning, and control. In this paper, we build such a …

Physics engines play an important role in robot planning and control; however, many real-
world control problems involve complex contact dynamics that cannot be characterized …

The force modulation of robotic manipulators has been extensively studied for several
decades. However, it is not yet commonly used in safety-critical applications due to a lack of …

Inspired by recent strides in empirical efficacy of implicit learning in many robotics tasks, we
seek to understand the theoretical benefits of implicit formulations in the face of nearly …

Robotic manipulation can greatly benefit from the data efficiency, robustness, and
predictability of model-based methods if robots can quickly generate models of novel objects …

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 …

A human's remarkable ability to manipulate unfamiliar objects with little prior knowledge of
them is a constant inspiration for robotics research. Despite the interest of the research …

This paper presents a problem of model learning for the purpose of learning how to navigate
a ball to a goal state in a circular maze environment with two degrees of freedom. The …