17 citations · 19 across the 7 of their papers we have counts for
8 papers
Cross-entropy optimization with prioritized constraints
Francisco Roldan Sanchez, Pau de las Heras Molins, David Fridovich-Keil +1
When constraints conflict, an optimizer must determine which requirements to preserve and which to relax. On the one hand, a priority ordering specifies which requirements take pre…
Dataset Clustering for Improved Offline Policy Learning
Qiang Wang, Yixin Deng, Francisco Roldan Sanchez +4
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training da…
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-base…
Improving Behavioural Cloning with Positive Unlabeled Learning
Qiang Wang, Robert McCarthy, David Cordova Bulens +6
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed q…
Hierarchical reinforcement learning for in-hand robotic manipulation using Davenport chained rotations
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
End-to-end reinforcement learning techniques are among the most successful methods for robotic manipulation tasks. However, the training time required to find a good policy capable…
Towards advanced robotic manipulation
Francisco Roldan Sanchez, Stephen Redmond, Kevin McGuinness +1
Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world app…
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy +7
This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipula…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…