Robotics and Cyber-Physical Systems

When designing policies or controllers for autonomous embodied systems, factors such as safety and data efficiency become paramount. For both low-level control and high-level planning problems, the standard practice has been to leverage symbolic domain knowledge (e.g., the governing equations of motion for the system, or an automaton representation of the high-level states) to design structured models that have certifiable guarantees, good generalization, or both (\eg, \citet{propel}). An emerging research direction is to automatically learn or discover the structure of the symbolic knowledge (\eg, \citet{xu2018neural}), which can be viewed as an instance of neurosymbolic programming.

Selected Publications

Dantam, Neil T.; Chaudhuri, Swarat; Kavraki, Lydia E.

The Task-Motion Kit: An Open Source, General-Purpose Task and Motion-Planning Framework Journal Article

In: IEEE Robotics Autom. Mag., vol. 25, no. 3, pp. 61–70, 2018.

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Wang, Yue; Chaudhuri, Swarat; Kavraki, Lydia E.

Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives Inproceedings

In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, pp. 238–246, 2018.

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Dantam, Neil T.; Kingston, Zachary K.; Chaudhuri, Swarat; Kavraki, Lydia E.

Incremental Task and Motion Planning: A Constraint-Based Approach Inproceedings

In: Robotics: Science and Systems XII, University of Michigan, Ann Arbor, Michigan, USA, June 18 - June 22, 2016, 2016.

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Nedunuri, Srinivas; Prabhu, Sailesh; Moll, Mark; Chaudhuri, Swarat; Kavraki, Lydia E.

SMT-based synthesis of integrated task and motion plans from plan outlines Inproceedings

In: 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, May 31 - June 7, 2014, pp. 655–662, 2014.

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Scientific Discovery

The acceleration of scientific discovery is among the most exciting promises of modern AI. However, algorithms that discover new scientific hypotheses and guide experiments must not only have high performance but also produce interpretable outputs. This makes many of our methods a natural fit for this space.

In particular, with collaborators in behavioral neuroscience, we have been recently working to use our methods in the analysis of animal behavior. Specifically, we have used neurosymbolic program synthesis to discover interpretable classifiers and clusters for behaviors, and models of divergences between different human experts annotating behaviors. In a separate ongoing effort with collaborators in cell biology, we are developing the use of program synthesis in discovering high-performance mechanistic models of RNA splicing.

Selected Publications

Jennifer J Sun Megan Tjandrasuwita, Ann Kennedy; Yue, Yisong

Interpreting Expert Annotation Differences in Animal Behavior Workshop



Shah, Ameesh; Zhan, Eric; Sun, Jennifer J.; Verma, Abhinav; Yue, Yisong; Chaudhuri, Swarat

Learning Differentiable Programs with Admissible Neural Heuristics Inproceedings

In: Larochelle, Hugo; Ranzato, Marc'Aurelio; Hadsell, Raia; Balcan, Maria-Florina; Lin, Hsuan-Tien (Ed.): Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.

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Software Engineering

Many aspects of everyday software engineering are repetitive; today, developers commonly perform these tasks using the guidance of other developers through forums like Stack Overflow. Program synthesis systems, such as the ones developed in our work, can potentially automate away many of these repetitive tasks. By doing so, they can allow the expert software engineer to focus on the more creative aspects of their work and enable novice programmers to do far more complex tasks than they can do today.

Selected Publications

Yeming Wen Rohan Mukherjee, Dipak Chaudhari; Jermaine, Chris

Neural Program Generation Modulo Static Analysis Journal Article

In: Neural Information Processing Systems (NeurIPS), 2021., 2021.


Mukherjee, Rohan; Jermaine, Chris; Chaudhuri, Swarat

Searching a Database of Source Codes Using Contextualized Code Search Journal Article

In: Proc. VLDB Endow., vol. 13, no. 10, pp. 1765–1778, 2020.

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Murali, Vijayaraghavan; Qi, Letao; Chaudhuri, Swarat; Jermaine, Chris

Neural Sketch Learning for Conditional Program Generation Inproceedings

In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, 2018.

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