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Fingerprint Dive into the research topics where Anirudha Majumdar is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 1 Similar Profiles
Motion planning Engineering & Materials Science
Controllers Engineering & Materials Science
Convex optimization Engineering & Materials Science
Robots Engineering & Materials Science
Feedback Engineering & Materials Science
Trajectories Engineering & Materials Science
Reinforcement learning Engineering & Materials Science
Polynomials Engineering & Materials Science

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Projects 2018 2022

Research Output 2012 2019

  • 259 Citations
  • 8 h-Index
  • 8 Conference contribution
  • 5 Article
  • 2 Conference article
  • 1 Paper

A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms

Singh, S., Chow, Y., Majumdar, A. & Pavone, M., Jul 2019, In : IEEE Transactions on Automatic Control. 64, 7, p. 2905-2912 8 p., 8485726.

Princeton University

Research output: Contribution to journalArticle

Model predictive control
Control theory
Convex optimization
Risk assessment
Linear systems

Task-Driven Estimation and Control via Information Bottlenecks

Pacelli, V. & Majumdar, A., May 2019, 2019 International Conference on Robotics and Automation, ICRA 2019. Institute of Electrical and Electronics Engineers Inc., p. 2061-2067 7 p. 8794213. (Proceedings - IEEE International Conference on Robotics and Automation; vol. 2019-May).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Robotics
Robots
State estimation
Brittleness
Pendulums

Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods

Singh, S., Lacotte, J., Majumdar, A. & Pavone, M., Dec 1 2018, In : International Journal of Robotics Research. 37, 13-14, p. 1713-1740 28 p.

Princeton University

Research output: Contribution to journalArticle

Nonparametric Methods
Reinforcement learning
Reinforcement Learning
Cost Function
Cost functions
31 Citations (Scopus)

Funnel libraries for real-time robust feedback motion planning

Majumdar, A. & Tedrake, R., Jul 1 2017, In : International Journal of Robotics Research. 36, 8, p. 947-982 36 p.

Princeton University

Research output: Contribution to journalArticle

Motion Planning
Motion planning
Robots
Feedback
Real-time

Risk-sensitive inverse reinforcement learning via coherent risk models

Majumdar, A., Singh, S., Mandlekar, A. & Pavone, M., Jan 1 2017, Robotics: Science and Systems XIII, RSS 2017. Srinivasa, S., Ayanian, N., Amato, N. & Kuindersma, S. (eds.). MIT Press Journals, (Robotics: Science and Systems; vol. 13).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reinforcement learning
Cost functions
Linear programming
Decision making