Highlights on Learning Cost Functions

  • [we often misspecify cost functions, but the cost we specify is a useful observation about the underlying true cost the robot should optimize] D. Hadfiled-Menell, S. Milli, P. Abbeel, S. Russell, and A.D. Dragan. Inverse Reward Design. Neural Information Processing Systems (NIPS), 2017. (oral, acceptance rate 1.2%)
  • [here we advocate that Inverse RL should be formulated as a collaboration in which the robot is no longer a passive observer, and the human is no longer an uninterested expert acting as if in isolation] D. Hadfield-Menell, A.D. Dragan, P. Abbeel, and S. Russell. "Cooperative Inverse Reinforcement Learning". Neural Information Processing Systems (NIPS), 2016.
  • [rather than solving CIRL via generic methods, here we dig into its structure to create faster solvers] D. Malik, M. Palaniappan, J. Fisac, D. Hadfield-Menell, S. Russell, and A. D. Dragan. An Efficient, Generalized Bellman Update for Cooperative Inverse Reinforcement Learning. International Conference on Machine Learning (ICML), 2018. (oral)
  • [there are many heuristics for responding to physical interaction from a human, but here we observe that pHRI is intentional and thus informative of the human's preferences for the task, thereby defining the notion of an optimal response; we also derive a real-time approximation] A. Bajcsy, D. Losey, M. O'Malley, and A.D. Dragan. Learning Robot Objectives from Physical Human Interaction. Conference on Robot Learning (CoRL), 2017. (oral, acceptance rate 10%)

Highlights on Learning to Coordinate with People

  • [there are many handcrafted strategies for enhancing coordination with people (e.g. cars inch forward at intersections); here we show that robots invent such strategies autonomously if they model their influence on human actions] D. Sadigh, S.S. Sastry, S.A. Seshia, and A.D. Dragan. "Information Gathering Actions over Human Internal State". International Conference on Intelligent Robots and Systems (IROS), 2016 (best cognitive robotics paper award finalist) , and "Planning for Autonomous Cars that Leverage Effects on Human Actions". Robotics: Science and Systems (RSS), 2016. (invited to special issue)
  • [much work focuses on better predictive models of people; but almost any model is bound to be wrong at times, and here we enable the robot to detect this online] J. Fisac, A. Bajcsy, D. Fridovich, S. Herbert, S. Wang, S. Milli, C. Tomlin, and A.D. Dragan. Probabilistically Safe Robot Planning with Confidence-Based Human Predictions. Robotics: Science and Systems (RSS), 2018. (invited to special issue)
  • [how worthwhile is it to be introducing mathematical models of human behavior? can we get away with learning human policies directly?] R. Choudhury, G. Swarmy, D. Hadfield-Menell, and A.D. Dragan. On the utility of model learning in HRI. International Confernece on Human-Robot Interaction (HRI), 2019.

All Conference Papers & Journal Articles