1 code implementation • 14 Feb 2024 • Qiang Wang, Yixin Deng, Francisco Roldan Sanchez, Keru Wang, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.
no code implementations • 8 Jul 2023 • Qiang Wang, Pablo Martinez Ulloa, Robert Burke, David Cordova Bulens, Stephen J. Redmond
When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96. 8%, providing timely feedback for stabilizing several practical gripping tasks.
1 code implementation • 30 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Francisco Roldan Sanchez, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond
However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory.
no code implementations • 27 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Nico Gürtler, Felix Widmaier, Francisco Roldan Sanchez, Stephen J. Redmond
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems.
no code implementations • 9 Aug 2022 • Han Ji, Qiang Wang, Stephen J. Redmond, Iman Tavakkolnia, Xiping Wu
In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users.
1 code implementation • 19 May 2022 • Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaier, Stefan Bauer, Stephen J. Redmond
Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.
no code implementations • 5 Oct 2021 • Robert McCarthy, Qiang Wang, Stephen J. Redmond
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts.
2 code implementations • 30 Sep 2021 • Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond
This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a challenge in which a three-fingered robot must carry a cube along specified goal trajectories.
no code implementations • 2 Oct 2019 • Luke Sy, Michael Raitor, Michael Del Rosario, Heba Khamis, Lauren Kark, Nigel H. Lovell, Stephen J. Redmond
Goal: This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.
Robotics Systems and Control Systems and Control