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Yixin Lin
Yixin Lin
Facebook AI Research
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Title
Cited by
Cited by
Year
Encoding physical constraints in differentiable newton-euler algorithm
G Sutanto, A Wang, Y Lin, M Mukadam, G Sukhatme, A Rai, F Meier
Learning for Dynamics and Control, 804-813, 2020
392020
Curious ilqr: Resolving uncertainty in model-based rl
S Bechtle, Y Lin, A Rai, L Righetti, F Meier
Conference on Robot Learning, 162-171, 2020
352020
Efficient and interpretable robot manipulation with graph neural networks
Y Lin, AS Wang, E Undersander, A Rai
IEEE Robotics and Automation Letters 7 (2), 2740-2747, 2022
202022
Polymetis
Y Lin, AS Wang, G Sutanto, A Rai, F Meier
102021
Learning state-dependent losses for inverse dynamics learning
K Morse, N Das, Y Lin, AS Wang, A Rai, F Meier
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
72020
Transformers are adaptable task planners
V Jain, Y Lin, E Undersander, Y Bisk, A Rai
Conference on Robot Learning, 1011-1037, 2023
62023
Rb2: Robotic manipulation benchmarking with a twist
S Dasari, J Wang, J Hong, S Bahl, Y Lin, A Wang, A Thankaraj, K Chahal, ...
arXiv preprint arXiv:2203.08098, 2022
52022
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
N Hansen, Y Lin, H Su, X Wang, V Kumar, A Rajeswaran
arXiv preprint arXiv:2212.05698, 2022
32022
Differentiable and learnable robot models
F Meier, A Wang, G Sutanto, Y Lin, P Shah
arXiv preprint arXiv:2202.11217, 2022
32022
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
T Shankar, Y Lin, A Rajeswaran, V Kumar, S Anderson, J Oh
International Conference on Machine Learning, 19626-19644, 2022
2022
Masked Trajectory Models for Prediction, Representation, and Control
P Wu, A Majumdar, K Stone, Y Lin, I Mordatch, P Abbeel, A Rajeswaran
Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 0
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
A Majumdar, K Yadav, S Arnaud, YJ Ma, C Chen, S Silwal, A Jain, ...
Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 0
Learning State-Dependent Losses for Inverse Dynamics Learning
N Das, K Morse, Y Lin, A Wang, A Rai, F Meier
Exploring by Exploiting Bad Models in Model-Based Reinforcement Learning
Y Lin, S Bechtle, L Righetti, A Rai, F Meier
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Articles 1–14