Reinforcement learning is typically concerned with learning control policies tailored to a
particular agent. We investigate whether there exists a single global policy that can …

The current dominant paradigm for robotic manipulation involves two separate stages:
manipulator design and control. Because the robot's morphology and how it can be …

Rigid body dynamics is a well-established frame--work in robotics. It can be used to expose
the analytic form of kinematic and dynamic functions of the robot model. So far, two major …

Robots are traditionally bound by a fixed morphology during their operational lifetime, which
is limited to adapting only their control strategies. Here we present the first quadrupedal …

In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent,
whose design is fixed, to maximize some notion of cumulative reward. The design of the …

The computational design of soft underwater swimmers is challenging because of the high
degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline …

We present a method for learning 3D geometry and physics parameters of a dynamic scene
from only a monocular RGB video input. To decouple the learning of underlying scene …

Robots are notoriously difficult to design because of complex interdependencies between
their physical structure, sensory and motor layouts, and behavior. Despite this, almost every …

The physical design of a robot and the policy that controls its motion are inherently coupled,
and should be determined according to the task and environment. In an increasing number …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …