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 …

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 …

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

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 …

Nuclear energy will play a critical role in meeting clean energy targets worldwide. However,
nuclear environments are dangerous for humans to operate in due to the presence of highly …

This paper shows how CasADi's state-of-the-art implementation of algorithmic differentiation
can be leveraged to formulate and efficiently solve gait optimization problems, enabling …