Thomas Howard joined the University of Rochester as an Assistant Professor of Electrical and Computer Engineering in 2015. Dr. Howard directs the University of Rochester's Robotics and Artificial Intelligence Laboratory, holds secondary appointments in the Department of Computer Science and the Department of Biomedical Engineering, and is a member of the Goergen Institute for Data Science. Previously he held appointments as a research scientist and a postdoctoral associate at MIT's Computer Science and Artificial Intelligence Laboratory in the Robust Robotics Group, a research technologist at NASA Jet Propulsion Laboratory in the Mobility and Robotic Systems section, and a lecturer in mechanical engineering at Caltech. Dr. Howard earned a PhD in robotics from The Robotics Institute at Carnegie Mellon University in 2009 in addition to BS degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. Dr. Howard currently teaches courses on Mechatronics and Embedded Systems (ECE 216), Robot Control (ECE 231), and Autonomous Mobile Robots (ECE 232).
Dr. Howard's research interests span artificial intelligence, robotics, and human-robot interaction with a research focus on improving the optimality, efficiency, and fidelity of models for decision making in complex and unstructured environments with applications to robot motion planning, grounded language communication, and assistive and medical robotics. He has applied his research on numerous robots including planetary rovers, autonomous automobiles, mobile manipulators, robotic torsos, and unmanned aerial vehicles. Dr. Howard was a member of the flight software team for the Mars Science Laboratory, the motion planning lead for the JPL/Caltech DARPA Autonomous Robotic Manipulation team, and a member of the motion planning team for Tartan Racing, winner of the 2007 DARPA Urban Challenge. Dr. Howard has earned Best Paper Awards at Robotics: Science and Systems (2016) and the IEEE International Conference on Systems, Man, and Cybernetics (2017), two NASA Group Achievement Awards (2012, 2014), was a finalist for the ICRA Best Manipulation Paper Award (2012), and was selected as a Goergen Institute for Data Science Center of Excellence Distinguished Researcher (2015-2017) and Wilmot Assistant Professor (2019-2021). Dr. Howard was also recognized with a NASA Early Career Faculty Award in 2019.
Visit the Robotics and Artificial Intelligence Laboratory website for a full list of publications.
T.M. Howard, E. Stump, J. Fink, J. Arkin, R. Paul, D. Park, S. Roy, D. Barber, R. Bendell, K. Schmeckpeper, J. Tian, J. Oh, M. Wigness, L. Quang, B. Rothrock, J. Nash, M. R. Walter, F. Jentsch, and N. Roy. An intelligence architecture for grounded language communication with field robots. Field Robotics, 2021. Forthcoming
B. Hedegaard, E. Fahnestock, J. Arkin, A. Menon, and T. M. Howard. Discrete optimization of adaptive state lattices for iterative motion planning on unmanned ground vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021. Forthcoming
N. Kent, R. M. Bhirangi, M. Travers, and T. M. Howard. Inferring task-space central pattern generator parameters for closed-loop control of underactuated robots. In 2020 IEEE International Conference on Robotics and Automation, pages 8833–8839, September 2020
J. Arkin, D. Park, S. Roy, M. Walter, N. Roy, T. M. Howard, and R. Paul. Multimodal estimation and communication of latent semantic knowledge for robust execution of robot instructions. International Journal of Robotics Research, 39(10-11):1279–1304, June 2020
S. Patki, A. Daniele, M. Walter, and T. M. Howard. Inferring compact representations for efficient natural language understanding of robot instructions. In 2019 IEEE International Conference on Robotics and Automation, pages 6926–6933, May 2019
M. Napoli, S. Goswami, S. McAleavey, M. Doyley, and T. M. Howard. Probabilistic mapping of tissue elasticity for robot-assisted medical ultrasound. In International Symposium on Robotics Research, October 2019. Forthcoming
S. Patki, E. Fahnestock, T. M. Howard, and M. Walter. Language-guided semantic mapping and mobile manipulation in partially observable environments. In Conference on Robot Learning, volume 100, pages 1201–1210. PMLR, October 2019
M. Napoli, H. Biggie, and T. M. Howard. Learning models for predictive adaptation in state lattices. In Field and Service Robotics: Results of the 11th International Conference. Springer Proceedings in Advanced Robotics, volume 5, pages 285–300. Springer, Cham, 2018
M. Esponda and T. M. Howard. Adaptive grasp control through multi-modal interactions for assistive prosthetic devices. In 5th AAAI Fall Symposium Series on Artificial Intelligence for Human-Robot Interaction, October 2018
M. Napoli, C. Freitas, S. Goswami, S. McAleavey, M. Doyley, and T. M. Howard. Hybrid force/velocity control with compliance estimation via strain elastography for robot assisted ultrasound screening. In 7th IEEE International Conference on Biomedical Robotics and Biomechatronics. IEEE, August 2018
R. Paul, J. Arkin, D. Aksaray, N. Roy, and T. M. Howard. Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms. International Journal of Robotics Research, 37(10):1269– 1299, June 2018
A. Broad, J. Arkin, N. Ratliff, T. M. Howard, and B. Argall. Real-time natural language corrections for assistive robotic manipulators. International Journal of Robotics Research, 36(5-7):684–698, May 2017
A. Boteanu, J. Arkin, T. M. Howard, and H. Kress-Gazit. A model for verifiable grounding and execution of complex language instructions. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2649–2654. IEEE, October 2016
T. Howard, S. Tellex, and N. Roy, “A natural language planner interface for mobile manipulators,” in 2014 IEEE International Conference on Robotics and Automation. IEEE, May 2014, pp. 6652–6659
T. M. Howard, M. Pivtoraiko, R. Knepper, and A. Kelly, “Model-predictive motion planning: several key developments for autonomous mobile robots,” IEEE Robotics and Automation Magazine, vol. 21, no. 1, pp. 64–73, Mar. 2014
N. Hudson, T. M. Howard, J. Ma, A. Jain, M. Bajracharya, S. Myint, L. Matthies, P. Backes, P. Hebert, T. Fuchs, and J. Burdick, “End-to-end dexterous manipulation with deliberative interactive estimation,” in 2012 IEEE International Conference on Robotics and Automation. IEEE, May 2012, pp. 2371–2378
D. Ferguson, T. M. Howard, and M. Likhachev, “Motion planning in urban environments,” Journal of Field Robotics, vol. 25, no. 11-12, pp. 939–960, 2008
C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer, M. Gittleman, S. Harbaugh, M. Hebert, T. M. Howard, S. Kolski, A. Kelly, M. Likhachev, M. McNaughton, N. Miller, K. Peterson, B. Pilnick, R. Rajkumar, P. Rybski, B. Salesky, Y. W. Seo, S. Singh, J. Snider, A. Stentz, W. Whittaker, Z. Wolkowicki, J. Ziglar, H. Bae, T. Brown, D. Demitrish, J. Litkouhi, B. Nickolaou, V. Sadekar, W. Zhang, J. Struble, M. Taylor, M. Darms, and D. Ferguson, “Autonomous driving in urban environments: Boss and the urban challenge,” Journal of Field Robotics, vol. 25, no. 8, pp. 425–466, 2008
T. M. Howard, C. Green, A. Kelly, and D. Ferguson, “State space sampling of feasible motions for high-performance mobile robot navigation in complex environments,” Journal of Field Robotics, vol. 25, no. 6-7, pp. 325–345, 2008
T. M. Howard and A. Kelly, "Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots," International Journal of Robotics Research. vol. 26, pp. 141-166, Feb. 2007