Anca D. Dragan

research

∙ 09/07/2023

Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning

Adaptive interfaces can help users perform sequential decision-making ta...

0 Jensen Gao, et al. ∙

research

∙ 07/19/2023

Contextual Reliability: When Different Features Matter in Different Contexts

Deep neural networks often fail catastrophically by relying on spurious ...

0 Gaurav Ghosal, et al. ∙

research

∙ 05/26/2023

Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization

As social media continues to have a significant influence on public opin...

0 Smitha Milli, et al. ∙

research

∙ 02/03/2023

Aligning Robot and Human Representations

To act in the world, robots rely on a representation of salient task asp...

0 Andreea Bobu, et al. ∙

research

∙ 01/03/2023

Benchmarks and Algorithms for Offline Preference-Based Reward Learning

Learning a reward function from human preferences is challenging as it t...

0 Daniel Shin, et al. ∙

research

∙ 01/02/2023

SIRL: Similarity-based Implicit Representation Learning

When robots learn reward functions using high capacity models that take ...

13 Andreea Bobu, et al. ∙

research

∙ 12/05/2022

Learning Representations that Enable Generalization in Assistive Tasks

Recent work in sim2real has successfully enabled robots to act in physic...

0 Jerry Zhi-Yang He, et al. ∙

research

∙ 08/23/2022

The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types

When inferring reward functions from human behavior (be it demonstration...

0 Gaurav R. Ghosal, et al. ∙

research

∙ 05/24/2022

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

How can we train an assistive human-machine interface (e.g., an electrom...

0 Siddharth Reddy, et al. ∙

research

∙ 04/13/2022

A Study of Causal Confusion in Preference-Based Reward Learning

Learning robot policies via preference-based reward learning is an incre...

2 Jeremy Tien, et al. ∙

research

∙ 03/04/2022

Teaching Robots to Span the Space of Functional Expressive Motion

Our goal is to enable robots to perform functional tasks in emotive ways...

0 Arjun Sripathy, et al. ∙

research

∙ 03/04/2022

X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback

We aim to help users communicate their intent to machines using flexible...

0 Jensen Gao, et al. ∙

research

∙ 02/05/2022

ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning

Building assistive interfaces for controlling robots through arbitrary, ...

0 Sean Chen, et al. ∙

research

∙ 01/18/2022

Inducing Structure in Reward Learning by Learning Features

Reward learning enables robots to learn adaptable behaviors from human i...

0 Andreea Bobu, et al. ∙

research

∙ 11/18/2021

Assisted Robust Reward Design

Real-world robotic tasks require complex reward functions. When we defin...

0 Jerry Zhi-Yang He, et al. ∙

research

∙ 09/29/2021

Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models

An outstanding challenge with safety methods for human-robot interaction...

0 Ran Tian, et al. ∙

research

∙ 07/07/2021

Pragmatic Image Compression for Human-in-the-Loop Decision-Making

Standard lossy image compression algorithms aim to preserve an image's a...

0 Siddharth Reddy, et al. ∙

research

∙ 07/06/2021

Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections

When a robot performs a task next to a human, physical interaction is in...

10 Dylan P. Losey, et al. ∙

research

∙ 06/11/2021

Policy Gradient Bayesian Robust Optimization for Imitation Learning

The difficulty in specifying rewards for many real-world problems has le...

17 Zaynah Javed, et al. ∙

research

∙ 05/05/2021

Preference learning along multiple criteria: A game-theoretic perspective

The literature on ranking from ordinal data is vast, and there are sever...

0 Kush Bhatia, et al. ∙

research

∙ 04/23/2021

Optimal Cost Design for Model Predictive Control

Many robotics domains use some form of nonconvex model predictive contro...

0 Avik Jain, et al. ∙

research

∙ 04/17/2021

Agnostic learning with unknown utilities

Traditional learning approaches for classification implicitly assume tha...

0 Kush Bhatia, et al. ∙

research

∙ 04/14/2021

Situational Confidence Assistance for Lifelong Shared Autonomy

Shared autonomy enables robots to infer user intent and assist in accomp...

0 Matthew Zurek, et al. ∙

research

∙ 03/13/2021

Dynamically Switching Human Prediction Models for Efficient Planning

As environments involving both robots and humans become increasingly com...

0 Arjun Sripathy, et al. ∙

research

∙ 03/09/2021

Analyzing Human Models that Adapt Online

Predictive human models often need to adapt their parameters online from...

11 Andrea Bajcsy, et al. ∙

research

∙ 03/09/2021

On complementing end-to-end human motion predictors with planning

High capacity end-to-end approaches for human motion prediction have the...

0 Liting Sun, et al. ∙

research

∙ 08/06/2020

Assisted Perception: Optimizing Observations to Communicate State

We aim to help users estimate the state of the world in tasks like robot...

6 Siddharth Reddy, et al. ∙

research

∙ 06/23/2020

Feature Expansive Reward Learning: Rethinking Human Input

In collaborative human-robot scenarios, when a person is not satisfied w...

0 Andreea Bobu, et al. ∙

research

∙ 02/12/2020

Reward-rational (implicit) choice: A unifying formalism for reward learning

It is often difficult to hand-specify what the correct reward function i...

0 Hong Jun Jeon, et al. ∙

research

∙ 02/03/2020

Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections

Human input has enabled autonomous systems to improve their capabilities...

0 Andreea Bobu, et al. ∙

research

∙ 01/13/2020

LESS is More: Rethinking Probabilistic Models of Human Behavior

Robots need models of human behavior for both inferring human goals and ...

45 Andreea Bobu, et al. ∙

research

∙ 12/05/2019

Learning Human Objectives by Evaluating Hypothetical Behavior

We seek to align agent behavior with a user's objectives in a reinforcem...

20 Siddharth Reddy, et al. ∙

research

∙ 11/06/2019

Nonverbal Robot Feedback for Human Teachers

Robots can learn preferences from human demonstrations, but their succes...

0 Sandy H. Huang, et al. ∙

research

∙ 10/29/2019

A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning

Real-world autonomous systems often employ probabilistic predictive mode...

0 Somil Bansal, et al. ∙

research

∙ 09/22/2019

Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

Autonomous robots often encounter challenging situations where their con...

0 Gokul Swamy, et al. ∙

research

∙ 09/10/2019

Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games

Differential games offer a powerful theoretical framework for formulatin...

0 David Fridovich-Keil, et al. ∙

research

∙ 07/27/2019

Bayesian Robustness: A Nonasymptotic Viewpoint

We study the problem of robustly estimating the posterior distribution f...

18 Kush Bhatia, et al. ∙

research

∙ 06/23/2019

On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference

Our goal is for agents to optimize the right reward function, despite ho...

0 Rohin Shah, et al. ∙

research

∙ 06/06/2019

An Extensible Interactive Interface for Agent Design

In artificial intelligence, we often specify tasks through a reward func...

0 Matthew Rahtz, et al. ∙

research

∙ 05/27/2019

SQIL: Imitation Learning via Regularized Behavioral Cloning

Learning to imitate expert behavior given action demonstrations containi...

0 Siddharth Reddy, et al. ∙

research

∙ 03/09/2019

Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

It is incredibly easy for a system designer to misspecify the objective ...

0 Smitha Milli, et al. ∙

research

∙ 12/21/2018

Human-AI Learning Performance in Multi-Armed Bandits

People frequently face challenging decision-making problems in which out...

0 Ravi Pandya, et al. ∙

research

∙ 12/04/2018

Learning from Intended Corrections

Our goal is to enable robots to learn cost functions from user guidance....

0 Jason Y. Zhang, et al. ∙

research

∙ 11/14/2018

A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance

Robust motion planning is a well-studied problem in the robotics literat...

0 Andrea Bajcsy, et al. ∙

research

∙ 10/18/2018

Establishing Appropriate Trust via Critical States

In order to effectively interact with or supervise a robot, humans need ...

0 Sandy H. Huang, et al. ∙

research

∙ 10/18/2018

Expressing Robot Incapability

Our goal is to enable robots to express their incapability, and to do so...

0 Minae Kwon, et al. ∙

research

∙ 10/13/2018

Hierarchical Game-Theoretic Planning for Autonomous Vehicles

The actions of an autonomous vehicle on the road affect and are affected...

0 Jaime F. Fisac, et al. ∙

research

∙ 10/11/2018

Learning under Misspecified Objective Spaces

Learning robot objective functions from human input has become increasin...

0 Andreea Bobu, et al. ∙

research

∙ 09/01/2018

Cost Functions for Robot Motion Style

We focus on autonomously generating robot motion for day to day physical...

0 Allan Zhou, et al. ∙

research

∙ 08/25/2018

The Social Cost of Strategic Classification

Consequential decision-making typically incentivizes individuals to beha...

0 Smitha Milli, et al. ∙

Anca D. Dragan

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