Zobrazeno 1 - 10
of 405
pro vyhledávání: '"Redmond, Stephen"'
Autor:
Wang, Qiang, Deng, Yixin, Sanchez, Francisco Roldan, Wang, Keru, McGuinness, Kevin, O'Connor, Noel, Redmond, Stephen J.
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training dataset is fixed, its quality becomes a crucial determining factor in th
Externí odkaz:
http://arxiv.org/abs/2402.09550
Autor:
Sanchez, Francisco Roldan, Wang, Qiang, Bulens, David Cordova, McGuinness, Kevin, Redmond, Stephen, O'Connor, Noel
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-based robotic manipulation tasks using sparse rewards. Even though HER imp
Externí odkaz:
http://arxiv.org/abs/2310.01827
Autor:
Wang, Qiang, Ulloa, Pablo Martinez, Burke, Robert, Bulens, David Cordova, Redmond, Stephen J.
The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accu
Externí odkaz:
http://arxiv.org/abs/2307.04011
Autor:
Wang, Qiang, McCarthy, Robert, Bulens, David Cordova, Sanchez, Francisco Roldan, McGuinness, Kevin, O'Connor, Noel E., Redmond, Stephen J.
This paper presents our solution for the Real Robot Challenge (RRC) III, a competition featured in the NeurIPS 2022 Competition Track, aimed at addressing dexterous robotic manipulation tasks through learning from pre-collected offline data. Particip
Externí odkaz:
http://arxiv.org/abs/2301.13019
Autor:
Wang, Qiang, McCarthy, Robert, Bulens, David Cordova, McGuinness, Kevin, O'Connor, Noel E., Gürtler, Nico, Widmaier, Felix, Sanchez, Francisco Roldan, Redmond, Stephen J.
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 quality, with a limited number of the trajectories that we would consid
Externí odkaz:
http://arxiv.org/abs/2301.11734
Autor:
Sanchez, Francisco Roldan, Wang, Qiang, Bulens, David Cordova, McGuinness, Kevin, Redmond, Stephen, O'Connor, Noel
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 of solving complex tasks is prohibitively large. Therefore, depending
Externí odkaz:
http://arxiv.org/abs/2210.00795
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 applications. This paper explores Hindsight Experience Replay both in sim
Externí odkaz:
http://arxiv.org/abs/2209.08903
Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks
Load balancing (LB) is a challenging issue in the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendl
Externí odkaz:
http://arxiv.org/abs/2208.05035