16  Deep RL for robotics

Work in progress

Agrawal et al. (2016), Dosovitskiy and Koltun (2016), Gu et al. (2017), Levine et al. (2016a), Levine et al. (2016b), Zhang et al. (2015)

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards (Schoettler et al., 2019)

State of the art: Amarjyoti (2017)

Agrawal, P., Nair, A., Abbeel, P., Malik, J., and Levine, S. (2016). Learning to Poke by Poking: Experiential Learning of Intuitive Physics. Available at: http://arxiv.org/abs/1606.07419.

Amarjyoti, S. (2017). Deep Reinforcement Learning for Robotic Manipulation-The state of the art. Available at: http://arxiv.org/abs/1701.08878.

Dosovitskiy, A., and Koltun, V. (2016). Learning to Act by Predicting the Future. Available at: http://arxiv.org/abs/1611.01779.

Gu, S., Holly, E., Lillicrap, T., and Levine, S. (2017). Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates. in Proc. ICRA Available at: http://arxiv.org/abs/1610.00633.

Levine, S., Finn, C., Darrell, T., and Abbeel, P. (2016a). End-to-End Training of Deep Visuomotor Policies. JMLR 17. Available at: http://arxiv.org/abs/1504.00702.

Levine, S., Pastor, P., Krizhevsky, A., and Quillen, D. (2016b). Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection. in Proc. ISER Available at: http://arxiv.org/abs/1603.02199.

Schoettler, G., Nair, A., Luo, J., Bahl, S., Ojea, J. A., Solowjow, E., et al. (2019). Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards. Available at: http://arxiv.org/abs/1906.05841 [Accessed June 18, 2019].

Zhang, F., Leitner, J., Milford, M., Upcroft, B., and Corke, P. (2015). Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control. in Proc. Acra Available at: http://arxiv.org/abs/1511.03791.