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 …

We present CasADi, an open-source software framework for numerical optimization. CasADi
is a general-purpose tool that can be used to model and solve optimization problems with a …

Constrained trajectory optimization has been a critical component in the development of
advanced guidance and control systems. An improperly planned reference trajectory can be …

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 …

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 …

We explore the dexterous manipulation transfer problem by designing simulators. The task
wishes to transfer human manipulations to dexterous robot hand simulations and is …

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 …

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 …

Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As
such, it is desirable to use a robot arm that has limited joint torques so as to not injure the …

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a
variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM …