We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

In the Learning and Intelligent Systems (LIS) group, our research brings together ideas from motion planning, machine learning and computer vision to synthesize robot systems that can behave intelligently across a wide range of problem domains. We are driven by the immense challenges faced by robots with imperfect sensors and incomplete knowledge of the world operating in unstructured environments.

Recent research areas we've focused on include:

  • Integrated Task and Motion Planning

  • Belief Space Planning

  • State Estimation

  • Learning and Optimization

  • Reinforcement Learning

  • Manipulation Planning

  • Grasping

  • Multiagent Planning

  • Partially Observable Markov Decision Processes (POMDP) 

Research Areas



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