11 citations · 11 across the 2 of their papers we have counts for
7 papers
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,…
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
Kevin Sebastian Luck, Mel Vecerik, Simon Stepputtis +2
Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of determ…
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov +13
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show h…
Generative predecessor models for sample-efficient imitation learning
Yannick Schroecker, Mel Vecerik, Jonathan Scholz
We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observe…
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
Mel Vecerik, Oleg Sushkov, David Barker +3
Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be hi…