15 citations · 30 across the 5 of their papers we have counts for
5 papers
DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots
Maria Bauza, Jose Enrique Chen, Valentin Dalibard +13
We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
Offline Meta-Reinforcement Learning for Industrial Insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov +5
Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper,…
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute +6
Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniqu…
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov +7
A robot's ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria,…