Robotics and Automation Research
Robotics and automation are advancing rapidly due to innovations in sensors, devices, UAVs, networks, optimization, and machine learning, accelerated by corporate and private investment. These systems have enormous potential to reduce drudgery and improve human experience in healthcare, manufacturing, transportation, safety, and a broad range of other applications, building on emerging advances in cloud computing, ensemble learning, big data, open-source software, and industry initiatives in the "Internet of Things", "Smarter Planet", "Industrial Internet" and "Industry 4.0." Recent developments in sequential non-convex optimization, model predictive control, partially observable Markov decision processes, reinforcement learning, and approximate probabilistic inference hold promise for addressing these problems at scale. Cloud Computing can provide access to large datasets and clusters of remote processors to filter, model, optimize, and share data across systems to improve performance over time.
Hoque, R., Seita, D., Balakrishna, A. et al. VisuoSpatial Foresight for physical sequential fabric manipulation. Auton Robot 46, 175–199 (2022). https://doi.org/10.1007/s10514-021-10001-0
Seita, Daniel & Jamali, Nawid & Laskey, Michael & Tanwani, Ajay & Berenstein, Ron & Baskaran, Prakash & Iba, Soshi & Canny, John & Goldberg, Kenneth. (2022). Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making. 10.1007/978-3-030-95459-8_17.
Wilcox, A., Balakrishna, A., Thananjeyan, B., Gonzalez, J.E., and Goldberg, K.. (2022). LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks. Proceedings of the 5th Conference on Robot Learning, in Proceedings of Machine Learning Research 164:959-969 Available from https://proceedings.mlr.press/v164/wilcox22a.html.
Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation
Y. Avigal et al., “Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation,” in IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1352-1364, July 2022, doi: 10.1109/TASE.2021.3138995.
Benjamin Insley, Ken Goldberg, Luc Beaulieu, Yunzhi Ma, Stephen McKinley, I-Chow Hsu, J. Adam Cunha, “Comparison of novel shielded nasopharynx applicator designs for intracavitary brachytherapy”, Brachytherapy, Volume 21, Issue 2, 2022, Pages 229-237, ISSN 1538-4721, https://doi.org/10.1016/j.brachy.2021.12.007.
Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
M. Hwang et al., “Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans,” in IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2022.3171795.
J. Ichnowski, Y. Avigal, Y. Liu and K. Goldberg, “GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 5255-5261, doi: 10.1109/ICRA46639.2022.9812387.
L. Fu et al., “LEGS: Learning Efficient Grasp Sets for Exploratory Grasping,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 8259-8265, doi: 10.1109/ICRA46639.2022.9812138.
A. Wilcox et al., “Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 9637-9643, doi: 10.1109/ICRA46639.2022.9812393.
H. Huang et al., “Mechanical Search on Shelves using a Novel “Bluction” Tool,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 6158-6164, doi: 10.1109/ICRA46639.2022.9811622.