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"Be wise, generalize!"
Planning is well known to be a hard problem. We are developing methods for acquiring useful knowledge while computing plans for small problem instances. This knowledge is then used to aid planning in larger, more difficult problems.
Often, our approaches can extract algorithmic, generalized plans that solve efficiently large classes of similar problems as well as problems with uncertainty in the quantities of objects that the agent needs to work with. The generalized plans we compute are easier to understand and are generated with proofs of correctness.
A real robot never has perfect sensors or actuators. Instead, an intelligent robot needs to be able to solve the tasks assigned to it while handling uncertainty about the environment as well as about the effects of its own actions. This is a challenging computational problem, but also one that humans solve on a routine basis (we don't have perfect sensors or actuators either!).
We are developing new methods for efficiently expressing and solving problems where the agent has limited, incomplete information about the quantities and identities of the objects that it may encounter.
The objective of this project is to introduce AI planning concepts using mobile manipulator robots. It uses a visual programming interface to make these concepts easier to grasp. Users can get the robot to accomplish desired tasks by dynamically populating puzzle shaped blocks encoding the robot's possible actions. This allows users to carry out navigation, planning and manipulation by connecting blocks instead of writing code. AI explanation techniques are used to inform a user if their plan to achieve a particular goal fails. This helps them better grasp the fundamentals of AI planning.
AI systems have the potential to improve our society in many walks of life. However, today’s AI systems require highly trained experts for their customization, configuration, and repair. This not only makes it difficult to realize the potential benefits of AI in society, but also creates large uncertainties in the future of employment for millions in the workforce.
To address these issues, we are developing new paradigms for computing user-aligned explanations of AI behavior. We are also developing well-defined AI systems that can talk to arbitrary, black-box AIs and derive a user-interpretable specification of the limits and capabilities of safe operation of the black-box AI. These methods facilitate spontaneous, productive teamwork between AI systems and people who may be experts in fields other than AI.
In order to solve complex, long-horizon tasks such as doing the laundry, a robot needs to compute high-level strategies (e.g., would it be useful to put all the dirty clothes in a basket first?) as well as the joint movements that it should execute. Unfortunately, approaches for high-level planning rely on task-planning abstractions that are lossy and can produce “solutions” that have no feasible executions.
We are developing new methods for computing safe task-planning abstractions and for dynamically refining the task-planning abstraction to produce combined task and motion plans that are guaranteed to be executable. We are also working on utilizing abstractions in sequential decision making (SDM) for evaluating the effect of abstractions on models for SDM, as well as to search for abstractions that would aid in solving a given SDM problem.
Assistant ProfessorDirector, AAIR Lab
|Shashank Rao Marpally||M.S.||Jan 2020 - May 2021|
|Deepak Kala Vasudevan||M.S.||Aug 2019 - Dec 2020|
|Abhyudaya Srinet||M.S.||Aug 2019 - Aug 2020|
|Kislay Kumar||M.S.||May 2018 - Dec 2019|
|Chirav Dave||M.S.||Dec 2017 - Dec 2019|
|Daniel Molina||M.S.||Aug 2017 - May 2019|
|Midhun P. M.||M.C.S.||Dec 2017 - Dec 2018|
|Julia Nakhleh||B.S.||Aug 2018 - May 2019|
|Ryan Christensen||B.S.||Aug 2017 - May 2018|
|Perry Wang||B.S.||Aug 2017 - May 2018|