43 citations · 184 across the 40 of their papers we have counts for
47 papers
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…
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
Apoorva Sharma, Sushant Veer, Asher Hancock +3
Inductive Conformal Prediction (ICP) provides a practical and effective approach for equipping deep learning models with uncertainty estimates in the form of set-valued predictions…
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…
Fundamental Tradeoffs in Learning with Prior Information
Anirudha Majumdar
We seek to understand fundamental tradeoffs between the accuracy of prior information that a learner has on a given problem and its learning performance. We introduce the notion of…
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…
Fast-response hot-wire flow sensors for wind and gust estimation on UAVs
Nathaniel Simon, Alexander Piqué, David Snyder +3
Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phe…
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…
Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy
Vincent Pacelli, Anirudha Majumdar
The rapid development of affordable and compact high-fidelity sensors (e.g., cameras and LIDAR) allows robots to construct detailed estimates of their states and environments. Howe…
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…
A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal, Elad Hazan, Anirudha Majumdar +1
We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new perform…
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…