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Mel Vecerik
Mel Vecerik
Adresă de e-mail confirmată pe ucl.ac.uk
Titlu
Citat de
Citat de
Anul
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
12252018
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
7832017
Sim-to-Real Robot Learning from Pixels with Progressive Nets
AR Andrei, V Mel, R Thomas, H Nicolas, P Razvan
1st Conference on Robot Learning, Mountain View, 2017
627*2017
Safe exploration in continuous action spaces
G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa
arXiv preprint arXiv:1801.08757, 2018
4882018
Learning from demonstrations for real world reinforcement learning
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ...
1792017
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
1382019
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
1372018
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
1172019
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
562021
TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
C Doersch, Y Yang, M Vecerik, D Gokay, A Gupta, Y Aytar, J Carreira, ...
arXiv preprint arXiv:2306.08637, 2023
512023
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
M Vecerik, JB Regli, O Sushkov, D Barker, R Pevceviciute, T Rothörl, ...
Proceedings of the 2020 Conference on Robot Learning 155, 449--460, 2020
392020
Generative predecessor models for sample-efficient imitation learning
Y Schroecker, M Vecerik, J Scholz
arXiv preprint arXiv:1904.01139, 2019
382019
A Framework for Data-Driven Robotics
S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ...
arXiv preprint arXiv:1909.12200, 2019
272019
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
M Vecerik, C Doersch, Y Yang, T Davchev, Y Aytar, G Zhou, R Hadsell, ...
arXiv preprint arXiv:2308.15975, 2023
132023
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
132020
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
Imitation learning using a generative predecessor neural network
M Vecerik, Y Schroecker, JK Scholz
US Patent 10,872,294, 2020
82020
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. 2017
M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ...
URL: http://arxiv. org/abs/1707.08817, 0
3
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
22022
Observe and look further: achieving consistent performance on Atari. CoRR abs/1805.11593 (2018)
T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ...
21805
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