ICRA 2021 Reading List
This is my personal post-ICRA 2021 reading list with papers I came across while attending the conference and which I particularly want to read. It is also intended as a resource for my colleagues who did not attend ICRA this year.
Most papers will be about task- or motion-level robot learning, but many intersect with other domains as well. If you presented a paper at ICRA you think I would like and it is not on this list, please send me an email and I promise to read it!
Highlighted are papers I read (or attended the presentation) and found especially insightful. This list is subject to change as I read my way through it or add more from the proceedings.
- T. Kulak, H. Girgin, J.-M. Odobez, and S. Calinon, “Active Learning of Bayesian Probabilistic Movement Primitives,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2163–2170, Apr. 2021, doi: 10.1109/LRA.2021.3060414.
- Y. Du, O. Watkins, T. Darrell, P. Abbeel, and D. Pathak, “Auto-Tuned Sim-to-Real Transfer,” arXiv:2104.07662 [cs], May 2021. Link
- S. Tosatto, G. Chalvatzaki, and J. Peters, “Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills,” arXiv:2010.13766 [cs], May 2021. Link
- S. Li, D. Park, Y. Sung, J. A. Shah, and N. Roy, “Reactive Task and Motion Planning under Temporal Logic Specifications,” arXiv:2103.14464 [cs], Mar. 2021. Link
- S. Luo, H. Kasaei, and L. Schomaker, “Self-Imitation Learning by Planning,” arXiv:2103.13834 [cs], Mar. 2021. Link
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- A. Sidiropoulos, Y. Karayiannidis, and Z. Doulgeri, “Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives,” arXiv:2104.03155 [cs], Apr. 2021. Link
- J.-S. Ha, Y.-J. Park, H.-J. Chae, S.-S. Park, and H.-L. Choi, “Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning,” arXiv:2011.08345 [cs], Apr. 2021. Link
- M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial Training is Not Ready for Robot Learning,” arXiv:2103.08187 [cs], Mar. 2021. Link
- E. Johns, “Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration,” arXiv:2105.06411 [cs], May 2021. Link
- M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes,” arXiv:2103.14127 [cs], Mar. 2021. Link
- A. S. Sathya, G. Pipeleers, W. Decré, and J. Swevers, “A Weighted Method for Fast Resolution of Strictly Hierarchical Robot Task Specifications Using Exact Penalty Functions,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3057–3064, Apr. 2021, doi: 10.1109/LRA.2021.3063026.
- T. Dinev, W. Merkt, V. Ivan, I. Havoutis, and S. Vijayakumar, “Sparsity-Inducing Optimal Control via Differential Dynamic Programming,” arXiv:2011.07325 [cs], Mar. 2021. Link
- T. Power and D. Berenson, “Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models,” arXiv:2102.02493 [cs], Feb. 2021. Link
- K. Boggess, S. Chen, and L. Feng, “Towards Personalized Explanation of Robot Path Planning via User Feedback,” arXiv:2011.00524 [cs], Mar. 2021. Link
- È. Pairet, C. Chamzas, Y. Petillot, and L. E. Kavraki, “Path Planning for Manipulation using Experience-driven Random Trees,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 3295–3302, Apr. 2021, doi: 10.1109/LRA.2021.3063063. Link
- H. Wen, X. Chen, G. Papagiannis, C. Hu, and Y. Li, “End-To-End Semi-supervised Learning for Differentiable Particle Filters,” arXiv:2011.05748 [cs, stat], Mar. 2021. Link
- M. Wulfmeier et al., “Representation Matters: Improving Perception and Exploration for Robotics,” arXiv:2011.01758 [cs, stat], Mar. 2021. Link
- J. Liu, W. Zeng, R. Urtasun, and E. Yumer, “Deep Structured Reactive Planning,” arXiv:2101.06832 [cs], Apr. 2021. Link
- A. Allshire, R. Martín-Martín, C. Lin, S. Manuel, S. Savarese, and A. Garg, “LASER: Learning a Latent Action Space for Efficient Reinforcement Learning,” arXiv:2103.15793 [cs], Mar. 2021. Link
- A. S. Chen, H. J. Nam, S. Nair, and C. Finn, “Batch Exploration with Examples for Scalable Robotic Reinforcement Learning,” IEEE Robot. Autom. Lett., vol. 6, no. 3, pp. 4401–4408, Jul. 2021, doi: 10.1109/LRA.2021.3068655. Link
- A. S. Morgan, D. Nandha, G. Chalvatzaki, C. D’Eramo, A. M. Dollar, and J. Peters, “Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning,” arXiv:2103.13842 [cs], Mar. 2021. Link
- T. E. Lee, J. Zhao, A. S. Sawhney, S. Girdhar, and O. Kroemer, “Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies,” arXiv:2103.16772 [cs], Mar. 2021. Link
- S. Bechtle, B. Hammoud, A. Rai, F. Meier, and L. Righetti, “Leveraging Forward Model Prediction Error for Learning Control,” arXiv:2011.03859 [cs], Nov. 2020. Link
- M. T. Akbulut, U. Bozdogan, A. Tekden, and E. Ugur, “Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization,” arXiv:2011.04282 [cs], Nov. 2020. Link
- C. Cioflan and R. Timofte, “MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search,” arXiv:2009.13940 [cs], Sep. 2020. Link
- Y. Wu, M. Mozifian, and F. Shkurti, “Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models,” arXiv:2011.01298 [cs], Nov. 2020. Link
- G. P. Meyer et al., “LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting,” arXiv:2003.05982 [cs], Oct. 2020. Link
- D. Ho, K. Rao, Z. Xu, E. Jang, M. Khansari, and Y. Bai, “RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer,” arXiv:2011.03148 [cs], Nov. 2020. Link
- A. Haidu and M. Beetz, “Automated acquisition of structured, semantic models of manipulation activities from human VR demonstration,” arXiv:2011.13689 [cs], Nov. 2020. Link
- M. Lutter, J. Silberbauer, J. Watson, and J. Peters, “Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning,” arXiv:2011.01734 [cs], Nov. 2020. Link
- Y. Lee, E. S. Hu, Z. Yang, A. Yin, and J. J. Lim, “IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks,” arXiv:1911.07246 [cs], Nov. 2019. Link
- D. Driess, J.-S. Ha, R. Tedrake, and M. Toussaint, “Learning Geometric Reasoning and Control for Long-Horizon Tasks from Visual Input,” p. 8.