1 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Vassil Atanassov, Wanming Yu, Siddhant Gangapurwala +2
Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to…
Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour
Guillaume Gagné-Labelle, Vassil Atanassov, Ioannis Havoutis
Climbing, crouching, bridging gaps, and walking up stairs are just a few of the advantages that quadruped robots have over wheeled robots, making them more suitable for navigating…
Discovery of skill switching criteria for learning agile quadruped locomotion
Wanming Yu, Fernando Acero, Vassil Atanassov +4
This paper develops a hierarchical learning and optimization framework that can learn and achieve well-coordinated multi-skill locomotion. The learned multi-skill policy can switch…
Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning
Vassil Atanassov, Wanming Yu, Alexander Luis Mitchell +2
Representation learning and unsupervised skill discovery can allow robots to acquire diverse and reusable behaviors without the need for task-specific rewards. In this work, we use…
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design
Vassil Atanassov, Jiatao Ding, Jens Kober +2
Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually…
Safe Model Predictive Control Approach for Non-holonomic Mobile Robots
Xinjie Liu, Vassil Atanassov
We design an model predictive control (MPC) approach for planning and control of non-holonomic mobile robots. Linearizing the system dynamics around the pre-computed reference traj…