Shikhar Bahl
Cited by
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Visual reinforcement learning with imagined goals
A Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine
arXiv preprint arXiv:1807.04742, 2018
Residual reinforcement learning for robot control
T Johannink, S Bahl, A Nair, J Luo, A Kumar, M Loskyll, JA Ojea, ...
2019 International Conference on Robotics and Automation (ICRA), 6023-6029, 2019
Skew-fit: State-covering self-supervised reinforcement learning
VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine
arXiv preprint arXiv:1903.03698, 2019
Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards
G Schoettler, A Nair, J Luo, S Bahl, JA Ojea, E Solowjow, S Levine
arXiv preprint arXiv:1906.05841, 2019
Contextual imagined goals for self-supervised robotic learning
A Nair, S Bahl, A Khazatsky, V Pong, G Berseth, S Levine
Conference on Robot Learning, 530-539, 2020
Impact on inequities in health indicators: Effect of implementing the integrated management of neonatal and childhood illness programme in Haryana, India
S Taneja, S Bahl, S Mazumder, J Martines, N Bhandari, MK Bhan
Journal of global health 5 (1), 2015
Neural dynamic policies for end-to-end sensorimotor learning
S Bahl, M Mukadam, A Gupta, D Pathak
arXiv preprint arXiv:2012.02788, 2020
Hierarchical Neural Dynamic Policies
S Bahl, A Gupta, D Pathak
arXiv preprint arXiv:2107.05627, 2021
RB2: Robotics Benchmarking with a Twist
S Dasari, J Wang, J Hong, S Bahl, Y Lin, A Wang, A Thankaraj, ...
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