Related Papers

Collection of relevant previous work on behavior priors in RL:

• Cang, C., Rajeswaran, A., Abbeel, P., & Laskin, M. (2021). Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL.

• Singh, A., Liu, H., Zhou, G., Yu, A., Rhinehart, N., & Levine, S. (2021). Parrot: Data-Driven Behavioral Priors for Reinforcement Learning.

• Rao, D., Sadeghi, F., Hasenclever, L., Wulfmeier, M., Zambelli, M., Vezzani, G., Tirumala, D., Aytar, Y., Merel, J., Heess, N.M., & Hadsell, R. (2021). Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies.

• J. Merel, L. Hasenclever, A. Galashov, A. Ahuja, V. Pham, G. Wayne, Y. W. Teh, and N. Heess. Neural probabilistic motor primitives for humanoid control. ICLR, 2019.

• J. Merel, S. Tunyasuvunakool, A. Ahuja, Y. Tassa, L. Hasenclever, V. Pham, T. Erez, G. Wayne, and N. Heess. Catch & carry: Reusable neural controllers for vision-guided whole-body tasks. ACM. Trans. Graph., 2020.

• T. Shankar, S. Tulsiani, L. Pinto, and A. Gupta. Discovering motor programs by recomposing demonstrations. In International Conference on Learning Representations, 2019.

• C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet. Learning latent plans from play. In Conference on Robot Learning, pages 1113–1132, 2020.

• K. Hausman, J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller. Learning an embedding space for transferable robot skills. In International Conference on Learning Representations, 2018.

• A. Sharma, S. Gu, S. Levine, V. Kumar, and K. Hausman. Dynamics-aware unsupervised discovery of skills. ICLR, 2020.

• Iscen, A., Caluwaerts, K., Tan, J., Zhang, T., Coumans, E., Sindhwani, V., & Vanhoucke, V. (2018, October). Policies modulating trajectory generators. In Conference on Robot Learning (pp. 916-926). PMLR.

• Jeong, R., Springenberg, J. T., Kay, J., Zheng, D., Zhou, Y., Galashov, A., ... & Nori, F. (2020). Learning Dexterous Manipulation from Suboptimal Experts. arXiv preprint arXiv:2010.08587.

• Pertsch, K., Lee, Y., & Lim, J. J. (2020). Accelerating Reinforcement Learning with Learned Skill Priors. arXiv preprint arXiv:2010.11944.

• Tirumala, D., Galashov, A., Noh, H., Hasenclever, L., Pascanu, R., Schwarz, J., ... & Heess, N. (2020). Behavior Priors for Efficient Reinforcement Learning. arXiv preprint arXiv:2010.14274.

• Fei Xia*, Chengshu Li*, Roberto Martín-Martín, Or Litany, Alexander Toshev, Silvio Savarese (2020). ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation. ICRA2021.

• Jeong, R., Springenberg, J. T., Kay, J., Zheng, D., Zhou, Y., Galashov, A., ... & Nori, F. (2020). Learning Dexterous Manipulation from Suboptimal Experts. arXiv preprint arXiv:2010.08587.

• Pertsch, K., Lee, Y., & Lim, J. J. (2020). Accelerating Reinforcement Learning with Learned Skill Priors. arXiv preprint arXiv:2010.11944.

• Zeng, Andy, et al. "Tossingbot: Learning to throw arbitrary objects with residual physics." IEEE Transactions on Robotics 36.4 (2020): 1307-1319.

• Silver, T., Allen, K., Tenenbaum, J., & Kaelbling, L. (2018). Residual policy learning. arXiv preprint arXiv:1812.06298.

• Schoettler, G., Nair, A., Luo, J., Bahl, S., Ojea, J. A., Solowjow, E., & Levine, S. (2019). Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards. arXiv preprint arXiv:1906.05841.

• M. A. Lee et al., "Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 7505-7512, doi: 10.1109/ICRA40945.2020.9197125.

• Cheng, R., Verma, A., Orosz, G., Chaudhuri, S., Yue, Y., & Burdick, J. (2019, May). Control regularization for reduced variance reinforcement learning. In International Conference on Machine Learning (pp. 1141-1150). PMLR.

• Srouji, M., Zhang, J., & Salakhutdinov, R. (2018, July). Structured control nets for deep reinforcement learning. In International Conference on Machine Learning (pp. 4742-4751). PMLR.

• Lee, Y., Sun, S. H., Somasundaram, S., Hu, E. S., & Lim, J. J. (2018, September). Composing complex skills by learning transition policies. In International Conference on Learning Representations.

• Rana, K., Dasagi, V., Talbot, B., Milford, M., and Sünderhauf, N. (2020, October). Multiplicative controller fusion: Leveraging algorithmic priors for sample-efficient reinforcement learning and safe sim-to-real transfer. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6069-6076). IEEE.

• Rana, K., Dasagi, V., Haviland, J., Talbot, B., Milford, M., and Sünderhauf, N. (2021). Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics. arXiv preprint arXiv:2107.09822.

• Peng, X. B., Chang, M., Zhang, G., Abbeel, P., and Levine, S. (2019). Mcp: Learning composable hierarchical control with multiplicative compositional policies. arXiv preprint arXiv:1905.09808.

• Tirumala, D., Noh, H., Galashov, A., Hasenclever, L., Ahuja, A., Wayne, G., ... and Heess, N. (2019). Exploiting hierarchy for learning and transfer in kl-regularized rl. arXiv preprint arXiv:1903.07438.