Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …

Modeling and manipulating elasto-plastic objects are essential capabilities for robots to
perform complex industrial and household interaction tasks (eg, stuffing dumplings, rolling …

Physics simulation is ubiquitous in robotics. Whether in model-based approaches (eg,
trajectory optimization), or model-free algorithms (eg, reinforcement learning), physics …

Differentiable simulators continue to push the state of the art across a range of domains
including computational physics, robotics, and machine learning. Their main value is the …

We present a differentiable pipeline for simulating the motion of objects that represent their
geometry as a continuous density field parameterized as a deep network. This includes …

Control of robots with kinematic constraints like loop-closure constraints or interactions with
the environment requires solving the underlying constrained dynamics equations of motion …

The incorporation of appropriate inductive bias plays a critical role in learning dynamics from
data. A growing body of work has been exploring ways to enforce energy conservation in the …

Collision detection is an important component of many robotics applications, from robot
control to simulation, including motion planning and estimation. While the seminal works on …

The flexibility of modern manufacturing processes such as additive manufacturing creates
an opportunity to build parts with customized material properties and geometries. However …