Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Abstract Machine learning (ML) has revolutionized robotics by enhancing perception,
adaptability, decision-making and more, enabling robots to work in complex scenarios …

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

Performing agile navigation with four-legged robots is a challenging task because of the
highly dynamic motions, contacts with various parts of the robot, and the limited field of view …

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

Parkour is a grand challenge for legged locomotion that requires robots to overcome various
obstacles rapidly in complex environments. Existing methods can generate either diverse …

In this letter, we propose a locomotion training framework where a control policy and a state
estimator are trained concurrently. The framework consists of a policy network which outputs …

Fast and stable locomotion of legged robots involves demanding and contradictory
requirements, in particular rapid control frequency as well as an accurate dynamics model …

In early 2016, we had the opportunity to test a pair of sprawling posture robots, one
designed to mimic a crocodile and another designed to mimic a monitor lizard, along the …

Recent years have seen a surge in commercially-available and affordable quadrupedal
robots, with many of these platforms being actively used in research and industry. As the …