Robotics and neuroscience are sister disciplines that both aim to understand how agile,
efficient, and robust locomotion can be achieved in autonomous agents. Robotics has …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

The interest in exploring planetary bodies for scientific investigation and in situ resource
utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art …

Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Successful real-world deployment of legged robots would require them to adapt in real-time
to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper …

Legged robots that can operate autonomously in remote and hazardous environments will
greatly increase opportunities for exploration into underexplored areas. Exteroceptive …

Legged locomotion can extend the operational domain of robots to some of the most
challenging environments on Earth. However, conventional controllers for legged …

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 …