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Mel Vecerik
Mel Vecerik
Verified email at ucl.ac.uk
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Year
Deep q-learning from demonstrations
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ...
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
8372018
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ...
arXiv preprint arXiv:1707.08817, 2017
5282017
Safe exploration in continuous action spaces
G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa
arXiv preprint arXiv:1801.08757, 2018
2742018
Learning from demonstrations for real world reinforcement learning
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ...
1542017
A practical approach to insertion with variable socket position using deep reinforcement learning
M Vecerik, O Sushkov, D Barker, T Rothörl, T Hester, J Scholz
2019 International Conference on Robotics and Automation (ICRA), 754-760, 2019
952019
Observe and look further: Achieving consistent performance on atari
T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ...
arXiv preprint arXiv:1805.11593, 2018
822018
Scaling data-driven robotics with reward sketching and batch reinforcement learning
S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ...
arXiv preprint arXiv:1909.12200, 2019
712019
Generative predecessor models for sample-efficient imitation learning
Y Schroecker, M Vecerik, J Scholz
arXiv preprint arXiv:1904.01139, 2019
282019
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
M Vecerik, JB Regli, O Sushkov, D Barker, R Pevceviciute, T Rothörl, ...
arXiv preprint arXiv:2009.14711, 2020
212020
A Framework for Data-Driven Robotics
S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ...
arXiv preprint arXiv:1909.12200, 2019
202019
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
J Luo, O Sushkov, R Pevceviciute, W Lian, C Su, M Vecerik, N Ye, ...
arXiv preprint arXiv:2103.11512, 2021
162021
Improved exploration through latent trajectory optimization in deep deterministic policy gradient
KS Luck, M Vecerik, S Stepputtis, HB Amor, J Scholz
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
102019
Data-efficient reinforcement learning for continuous control tasks
M Riedmiller, R Hafner, M Vecerik, TP Lillicrap, T Lampe, I Popov, ...
US Patent 10,664,725, 2020
52020
Neural networks for selecting actions to be performed by a robotic agent
R Pascanu, RT Hadsell, M Vecerik, T Rothoerl, AA Rusu, NMO Heess
US Patent 10,632,618, 2020
52020
Training action selection neural networks using apprenticeship
O Pietquin, M Riedmiller, W Fumin, B Piot, M Vecerik, TA Hester, T Rothörl, ...
US Patent App. 16/624,245, 2020
42020
Imitation learning using a generative predecessor neural network
M Vecerik, Y Schroecker, JK Scholz
US Patent 10,872,294, 2020
12020
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
M Vecerik, J Kay, R Hadsell, L Agapito, J Scholz
2022 International Conference on Robotics and Automation (ICRA), 1251-1257, 2022
2022
Data-driven robot control
S Cabi, Z Wang, A Novikov, K Konyushkova, SG Colmenarejo, SE Reed, ...
US Patent App. 17/020,294, 2021
2021
Data-efficient reinforcement learning for continuous control tasks
M Riedmiller, R Hafner, M Vecerik, TP Lillicrap, T Lampe, I Popov, ...
US Patent App. 16/882,373, 2020
2020
Neural networks for selecting actions to be performed by a robotic agent
R Pascanu, RT Hadsell, M Vecerik, T Rothoerl, AA Rusu, NMO Heess
US Patent App. 16/829,237, 2020
2020
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Articles 1–20