43 citations · 184 across the 40 of their papers we have counts for
27 papers · 1 filter
Predictive Red Teaming: Breaking Policies Without Breaking Robots
Anirudha Majumdar, Mohit Sharma, Dmitry Kalashnikov +3
Visuomotor policies trained via imitation learning are capable of performing challenging manipulation tasks, but are often extremely brittle to lighting, visual distractors, and ob…
Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception
Zhiting Mei, Anushri Dixit, Meghan Booker +5
Rapid advances in perception have enabled large pre-trained models to be used out of the box for transforming high-dimensional, noisy, and partial observations of the world into ri…
Privacy-Preserving Map-Free Exploration for Confirming the Absence of a Radioactive Source
Eric Lepowsky, David Snyder, Alexander Glaser +1
Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms contr…
MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction
Nathaniel Simon, Anirudha Majumdar
A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV) platforms (< 100 g) is their inability to carry sensors that provide high-resolution metric depth informat…
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova +11
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remai…
Online Learning for Obstacle Avoidance
David Snyder, Meghan Booker, Nathaniel Simon +4
We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations…
AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel +1
Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to…
Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions
Meghan Booker, Anirudha Majumdar
Motivated by the goal of endowing robots with a means for focusing attention in order to operate reliably in complex, uncertain, and time-varying environments, we consider how a ro…
FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors
Nathaniel Simon, Allen Z. Ren, Alexander Piqué +4
Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional m…
Leveraging Language for Accelerated Learning of Tool Manipulation
Allen Z. Ren, Bharat Govil, Tsung-Yen Yang +2
Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools. We investigate whether linguistic information about a tool…
Towards a Framework for Comparing the Complexity of Robotic Tasks
Michelle Ho, Alec Farid, Anirudha Majumdar
We are motivated by the problem of comparing the complexity of one robotic task relative to another. To this end, we define a notion of reduction that formalizes the following intu…
Failure Prediction with Statistical Guarantees for Vision-Based Robot Control
Alec Farid, David Snyder, Allen Z. Ren +1
We are motivated by the problem of performing failure prediction for safety-critical robotic systems with high-dimensional sensor observations (e.g., vision). Given access to a bla…
Fundamental Limits for Sensor-Based Robot Control
Anirudha Majumdar, Zhiting Mei, Vincent Pacelli
Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot's sensors for a given task. In order to achieve this, we define a…
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees
Kai-Chieh Hsu, Allen Z. Ren, Duy Phuong Nguyen +2
Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using rei…
Learning Provably Robust Motion Planners Using Funnel Libraries
Ali Ekin Gurgen, Anirudha Majumdar, Sushant Veer
This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturban…
Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data
Abhinav Agarwal, Sushant Veer, Allen Z. Ren +1
We are motivated by the problem of learning policies for robotic systems with rich sensory inputs (e.g., vision) in a manner that allows us to guarantee generalization to environme…
Distributionally Robust Policy Learning via Adversarial Environment Generation
Allen Z. Ren, Anirudha Majumdar
Our goal is to train control policies that generalize well to unseen environments. Inspired by the Distributionally Robust Optimization (DRO) framework, we propose DRAGEN - Distrib…
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning
Alec Farid, Sushant Veer, Divyanshu Pachisia +1
Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to trai…
Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
Paula Gradu, John Hallman, Daniel Suo +7
We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite. The introdu…
Learning to Actively Reduce Memory Requirements for Robot Control Tasks
Meghan Booker, Anirudha Majumdar
Robots equipped with rich sensing modalities (e.g., RGB-D cameras) performing long-horizon tasks motivate the need for policies that are highly memory-efficient. State-of-the-art a…
Generalization Guarantees for Imitation Learning
Allen Z. Ren, Sushant Veer, Anirudha Majumdar
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to acc…
Probably Approximately Correct Vision-Based Planning using Motion Primitives
Sushant Veer, Anirudha Majumdar
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Proba…
PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments
Anirudha Majumdar, Alec Farid, Anoopkumar Sonar
Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our appr…
How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics
Anirudha Majumdar, Marco Pavone
Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty.…