Anirudha Majumdar

research

∙ 06/14/2023

Online Learning for Obstacle Avoidance

We approach the fundamental problem of obstacle avoidance for robotic sy...

0 David Snyder, et al. ∙

research

∙ 04/26/2023

Fundamental Tradeoffs in Learning with Prior Information

We seek to understand fundamental tradeoffs between the accuracy of prio...

0 Anirudha Majumdar, et al. ∙

research

∙ 02/09/2023

AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer

Simulation parameter settings such as contact models and object geometry...

0 Allen Z. Ren, et al. ∙

research

∙ 11/10/2022

Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions

Motivated by the goal of endowing robots with a means for focusing atten...

0 Meghan Booker, et al. ∙

research

∙ 10/12/2022

FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors

Unmanned aerial vehicles (UAVs) are finding use in applications that pla...

0 Nathaniel Simon, et al. ∙

research

∙ 06/27/2022

Leveraging Language for Accelerated Learning of Tool Manipulation

Robust and generalized tool manipulation requires an understanding of th...

0 Allen Z. Ren, et al. ∙

research

∙ 02/20/2022

Towards a Framework for Comparing the Complexity of Robotic Tasks

We are motivated by the problem of comparing the complexity of one robot...

0 Michelle Ho, et al. ∙

research

∙ 02/11/2022

Failure Prediction with Statistical Guarantees for Vision-Based Robot Control

We are motivated by the problem of performing failure prediction for saf...

0 Alec Farid, et al. ∙

research

∙ 01/31/2022

Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning

Our goal is to develop theory and algorithms for establishing fundamenta...

0 Anirudha Majumdar, et al. ∙

research

∙ 01/20/2022

Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees

Safety is a critical component of autonomous systems and remains a chall...

4 Kai-Chieh Hsu, et al. ∙

research

∙ 11/16/2021

Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data

We are motivated by the problem of learning policies for robotic systems...

7 Abhinav Agarwal, et al. ∙

research

∙ 11/16/2021

Learning Provably Robust Motion Planners Using Funnel Libraries

This paper presents an approach for learning motion planners that are ac...

2 Ali Ekin Gurgen, et al. ∙

research

∙ 09/17/2021

Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy

The rapid development of affordable and compact high-fidelity sensors (e...

0 Vincent Pacelli, et al. ∙

research

∙ 07/13/2021

Distributionally Robust Policy Learning via Adversarial Environment Generation

Our goal is to train control policies that generalize well to unseen env...

0 Allen Z. Ren, et al. ∙

research

∙ 06/25/2021

Task-Driven Out-of-Distribution Detection with Statistical Guarantees for Robot Learning

Our goal is to perform out-of-distribution (OOD) detection, i.e., to det...

8 Alec Farid, et al. ∙

research

∙ 02/26/2021

A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based po...

11 Naman Agarwal, et al. ∙

research

∙ 02/19/2021

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

We present an open-source library of natively differentiable physics and...

30 Paula Gradu, et al. ∙

research

∙ 02/12/2021

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

We are motivated by the problem of providing strong generalization guara...

0 Alec Farid, et al. ∙

research

∙ 12/12/2020

Generating Adversarial Disturbances for Controller Verification

We consider the problem of generating maximally adversarial disturbances...

0 Udaya Ghai, et al. ∙

research

∙ 10/24/2020

LagNetViP: A Lagrangian Neural Network for Video Prediction

The dominant paradigms for video prediction rely on opaque transition mo...

9 Christine Allen-Blanchette, et al. ∙

research

∙ 08/17/2020

Learning to Actively Reduce Memory Requirements for Robot Control Tasks

Robots equipped with rich sensing modalities (e.g., RGB-D cameras) perfo...

0 Meghan Booker, et al. ∙

research

∙ 08/05/2020

Generalization Guarantees for Multi-Modal Imitation Learning

Control policies from imitation learning can often fail to generalize to...

0 Allen Z. Ren, et al. ∙

research

∙ 07/16/2020

CoNES: Convex Natural Evolutionary Strategies

We present a novel algorithm – convex natural evolutionary strategies (C...

6 Sushant Veer, et al. ∙

research

∙ 06/01/2020

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

A fundamental challenge in reinforcement learning is to learn policies t...

10 Anoopkumar Sonar, et al. ∙

research

∙ 02/28/2020

Probably Approximately Correct Vision-Based Planning using Motion Primitives

This paper presents a deep reinforcement learning approach for synthesiz...

0 Sushant Veer, et al. ∙

research

∙ 02/04/2020

Learning Task-Driven Control Policies via Information Bottlenecks

This paper presents a reinforcement learning approach to synthesizing ta...

0 Vincent Pacelli, et al. ∙

research

∙ 08/14/2019

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...

0 Anirudha Majumdar, et al. ∙

research

∙ 09/20/2018

Task-Driven Estimation and Control via Information Bottlenecks

Our goal is to develop a principled and general algorithmic framework fo...

0 Vincent Pacelli, et al. ∙

research

∙ 06/11/2018

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

Our goal is to synthesize controllers for robots that provably generaliz...

0 Anirudha Majumdar, et al. ∙

research

∙ 11/28/2017

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

The literature on Inverse Reinforcement Learning (IRL) typically assumes...

0 Sumeet Singh, et al. ∙

research

∙ 10/30/2017

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...

0 Anirudha Majumdar, et al. ∙

research

∙ 10/09/2017

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

In a recent note [8], the author provides a counterexample to the global...

0 Amir Ali Ahmadi, et al. ∙

research

∙ 06/08/2017

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...

0 Amir Ali Ahmadi, et al. ∙

research

∙ 01/15/2016

Funnel Libraries for Real-Time Robust Feedback Motion Planning

We consider the problem of generating motion plans for a robot that are ...

0 Anirudha Majumdar, et al. ∙

Anirudha Majumdar

Featured Co-authors