This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …

The empirical success of derivative-free methods in reinforcement learning for planning
through contact seems at odds with the perceived fragility of classical gradient-based …

This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled
bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a …

We present a framework for bi-level trajectory optimization in which a system's dynamics are
encoded as the solution to a constrained optimization problem and smooth gradients of this …

Dexterous manipulation tasks often require contact switching, where fingers make and break
contact with the object. We propose a method that plans trajectories for dexterous …

To fully utilize the versatility of a multi-fingered dexterous robotic hand for executing diverse
object grasps, one must consider the rich physical constraints introduced by hand-object …

This paper focuses on robustness to disturbance forces and uncertain payloads. We present
a novel formulation to optimize the robustness of dynamic trajectories. A straightforward …

The central theme in robotic manipulation is that of the robot interacting with the world
through physical contact. We tend to describe that physical contact using specific words that …

We present a bilevel, contact-implicit trajectory optimization (TO) formulation that searches
for robot trajectories with learned soft contact models. On the lower-level, contact forces are …

Optimization problems over dynamic networks have been extensively studied and widely
used in the past decades to formulate numerous real-world problems. However,(1) …