Anirudha Majumdar

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MIT ∙ Princeton University

A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based po...
02/26/2021 ∙ by Naman Agarwal, et al. ∙ 11 ∙ share

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Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking

We present an open-source library of natively differentiable physics and...
02/19/2021 ∙ by Paula Gradu, et al. ∙ 30 ∙ share

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PAC-BUS: Meta-Learning Bounds via PAC-Bayes and Uniform Stability

We are motivated by the problem of providing strong generalization guara...
02/12/2021 ∙ by Alec Farid, et al. ∙ 0 ∙ share

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Generating Adversarial Disturbances for Controller Verification

We consider the problem of generating maximally adversarial disturbances...
12/12/2020 ∙ by Udaya Ghai, et al. ∙ 0 ∙ share

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LagNetViP: A Lagrangian Neural Network for Video Prediction

The dominant paradigms for video prediction rely on opaque transition mo...
10/24/2020 ∙ by Christine Allen-Blanchette, et al. ∙ 9 ∙ share

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Learning to Actively Reduce Memory Requirements for Robot Control Tasks

Robots equipped with rich sensing modalities (e.g., RGB-D cameras) perfo...
08/17/2020 ∙ by Meghan Booker, et al. ∙ 0 ∙ share

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Generalization Guarantees for Multi-Modal Imitation Learning

Control policies from imitation learning can often fail to generalize to...
08/05/2020 ∙ by Allen Z. Ren, et al. ∙ 0 ∙ share

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CoNES: Convex Natural Evolutionary Strategies

We present a novel algorithm – convex natural evolutionary strategies (C...
07/16/2020 ∙ by Sushant Veer, et al. ∙ 6 ∙ share

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Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

A fundamental challenge in reinforcement learning is to learn policies t...
06/01/2020 ∙ by Anoopkumar Sonar, et al. ∙ 10 ∙ share

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Probably Approximately Correct Vision-Based Planning using Motion Primitives

This paper presents a deep reinforcement learning approach for synthesiz...
02/28/2020 ∙ by Sushant Veer, et al. ∙ 0 ∙ share

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Learning Task-Driven Control Policies via Information Bottlenecks

This paper presents a reinforcement learning approach to synthesizing ta...
02/04/2020 ∙ by Vincent Pacelli, et al. ∙ 0 ∙ share

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A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

Historically, scalability has been a major challenge to the successful a...
08/14/2019 ∙ by Anirudha Majumdar, et al. ∙ 0 ∙ share

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Task-Driven Estimation and Control via Information Bottlenecks

Our goal is to develop a principled and general algorithmic framework fo...
09/20/2018 ∙ by Vincent Pacelli, et al. ∙ 0 ∙ share

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PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments

Our goal is to synthesize controllers for robots that provably generaliz...
06/11/2018 ∙ by Anirudha Majumdar, et al. ∙ 0 ∙ share

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Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

The literature on Inverse Reinforcement Learning (IRL) typically assumes...
11/28/2017 ∙ by Sumeet Singh, et al. ∙ 0 ∙ share

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How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

Endowing robots with the capability of assessing risk and making risk-aw...
10/30/2017 ∙ by Anirudha Majumdar, et al. ∙ 0 ∙ share

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Response to "Counterexample to global convergence of DSOS and SDSOS hierarchies"

In a recent note [8], the author provides a counterexample to the global...
10/09/2017 ∙ by Amir Ali Ahmadi, et al. ∙ 0 ∙ share

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DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization

In recent years, optimization theory has been greatly impacted by the ad...
06/08/2017 ∙ by Amir Ali Ahmadi, et al. ∙ 0 ∙ share

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Funnel Libraries for Real-Time Robust Feedback Motion Planning

We consider the problem of generating motion plans for a robot that are ...
01/15/2016 ∙ by Anirudha Majumdar, et al. ∙ 0 ∙ share

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Anirudha Majumdar

Featured Co-authors

Marco Pavone

Elad Hazan

Russ Tedrake

Karan Singh

Naman Agarwal

Cyril Zhang

Amir Ali Ahmadi

Naomi Ehrich Leonard

Georgina Hall

Jonathan Lacotte

Udaya Ghai

A Regret Minimization Approach to Iterative Learning Control

Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking

PAC-BUS: Meta-Learning Bounds via PAC-Bayes and Uniform Stability

Generating Adversarial Disturbances for Controller Verification

LagNetViP: A Lagrangian Neural Network for Video Prediction

Learning to Actively Reduce Memory Requirements for Robot Control Tasks

Generalization Guarantees for Multi-Modal Imitation Learning

CoNES: Convex Natural Evolutionary Strategies

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

Probably Approximately Correct Vision-Based Planning using Motion Primitives

Learning Task-Driven Control Policies via Information Bottlenecks

A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

Task-Driven Estimation and Control via Information Bottlenecks

PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments

Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

Response to "Counterexample to global convergence of DSOS and SDSOS hierarchies"

DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization

Funnel Libraries for Real-Time Robust Feedback Motion Planning