PeopleofSuperintelligence

Researcher

Anirudha Majumdar

Anirudha Majumdar is a robotics researcher whose work uses machine learning to help robots plan and act safely in unfamiliar settings. He earned a Ph.D. in electrical engineering and computer science at MIT and an undergraduate degree in mechanical engineering and mathematics at Penn, and worked as a postdoctoral candidate at Stanford's Autonomous Systems Lab from 2016 to 2017. At Princeton, he leads the Intelligent Robot Motion Lab and coauthored KnowNo, a conformal-prediction framework that measures uncertainty in language-model planners and reduces the need for human help. In March 2026, he presented Google DeepMind research on video world models, and Princeton named him an inaugural co-director of Princeton Robotics in April 2026. As of October 2026, he is an associate professor of mechanical and aerospace engineering at Princeton.

Works
122
Citations
2,823
h-index
25

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2013–2016

    An ICRA award and motion planning

    In 2013, his research earns the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA). In 2016, he and Russ Tedrake post the arXiv paper Funnel Libraries for Real-Time Robust Feedback Motion Planning, which precomputes motion funnels and composes safe plans under uncertainty.

  2. 2016–2017

    Stanford postdoc and MIT doctorate

    His education includes a Ph.D. in electrical engineering and computer science from MIT with Russ Tedrake and an undergraduate degree in mechanical engineering and mathematics from the University of Pennsylvania, where he is a member of the GRASP Lab. From 2016 to 2017, he works as a postdoctoral candidate at Stanford's Autonomous Systems Lab.

  3. 2017–2023

    Princeton faculty and lab

    He joins Princeton's Mechanical and Aerospace Engineering faculty in 2017. By 2023, he leads the Intelligent Robot Motion Lab and studies how autonomous robots can see obstacles and adapt to diverse, changing environments.

  4. 2020

    Machine learning for robot safety

    In 2020, his group adapts machine-learning frameworks for robot locomotion and manipulation, using generalization theory to provide performance guarantees in unfamiliar environments.

  5. 2022

    Safe reinforcement learning

    In 2022, he coauthors Sim-to-Lab-to-Real, a safe reinforcement-learning framework that shields unsafe actions during exploration and uses PAC-Bayes bounds for policy safety and performance in unseen environments.

  6. 2023

    KnowNo for robot planners

    In 2023, he coauthors KnowNo, a framework that uses conformal prediction to measure uncertainty in large language model planners, provide statistical guarantees on task completion, and minimize human help.

  7. 2023

    A Sloan Research Fellowship

    He receives the 2023 Sloan Research Fellowship in computer science.

  8. March 2026

    World models for robot policy evaluation

    In March 2026, he presents Trustworthy World Models for Safe Generalist Robots as a visiting research scientist at Google DeepMind and an associate professor at Princeton. He describes using video models as simulators for evaluating generalist robot policies for performance, generalization, and safety.

  9. April 2026

    Co-directing Princeton Robotics

    In April 2026, Princeton appoints him and Aimy Wissa inaugural co-directors of Princeton Robotics, a research initiative that fosters collaboration across engineering and the broader campus.

  10. As of October 2026

    Associate professor at Princeton

    As of October 2026, he is an associate professor of mechanical and aerospace engineering at Princeton. His research focuses on controlling highly agile robotic systems such as unmanned aerial vehicles, with formal guarantees on their safety and performance.

Career

  1. NowPrinceton UniversityAssociate professor
  2. 2026Google DeepMindVisiting research scientist
  3. 2016–2017Stanford UniversityPostdoctoral candidate
  4. University of PennsylvaniaMember

Education

  1. Massachusetts Institute of TechnologyPh.D. in electrical engineering and computer science
  2. University of PennsylvaniaUndergraduate degree in mechanical engineering and mathematics

From the biography.

Checked career sources: engineering.princeton.edu, engineering.princeton.edu.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Anirudha Majumdar - Stanford ASL stanfordasl.github.io. Checked 26 October 2026.
  2. [1601.04037] Funnel Libraries for Real-Time Robust Feedback Motion Planning arxiv.org. Checked 26 October 2026.
  3. Heide and Majumdar awarded 2023 Sloan Fellowships - Princeton Engineering engineering.princeton.edu. Checked 26 October 2026.
  4. Machine learning guarantees robots’ performance in unknown territory - Princeton Engineering engineering.princeton.edu. Checked 26 October 2026.
  5. [2201.08355] Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees arxiv.org. Checked 26 October 2026.
  6. [2307.01928] Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners arxiv.org. Checked 26 October 2026.
  7. Winter 2026 | Mila Robot Learning Seminar montrealrobotics.ca. Checked 26 October 2026.
  8. Majumdar and Wissa are leading growth in ‘humanity-driven robotics’ - Princeton Engineering engineering.princeton.edu. Checked 26 October 2026.
  9. Anirudha Majumdar - Princeton Engineering engineering.princeton.edu. Checked 26 October 2026.