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Fingerprint Fingerprint is based on mining the text of the experts' scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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

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Research Output 2012 2018

  • 217 Citations
  • 8 h-Index
  • 7 Conference contribution
  • 5 Article
  • 2 Paper
  • 2 Conference article

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

Singh, S., Chow, Y., Majumdar, A. & Pavone, M., Jan 1 2018, (Accepted/In press) In : IEEE Transactions on Automatic Control.

Princeton University

Research output: Contribution to journalArticle

Model predictive control
Control theory
Convex optimization
Risk assessment
Linear systems

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
17 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.

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
14 Citations (Scopus)

Robust online motion planning via contraction theory and convex optimization

Singh, S., Majumdar, A., Slotine, J. J. & Pavone, M., Jul 21 2017, ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., p. 5883-5890 8 p. 7989693. (Proceedings - IEEE International Conference on Robotics and Automation).

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

Convex optimization
Motion planning
Trajectories
Model predictive control
Unmanned aerial vehicles (UAV)