research
∙
09/07/2023
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
Adaptive interfaces can help users perform sequential decision-making ta...
share
research
∙
07/19/2023
Contextual Reliability: When Different Features Matter in Different Contexts
Deep neural networks often fail catastrophically by relying on spurious ...
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...
research
∙
02/03/2023
Aligning Robot and Human Representations
To act in the world, robots rely on a representation of salient task asp...
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...
research
∙
01/02/2023
SIRL: Similarity-based Implicit Representation Learning
When robots learn reward functions using high capacity models that take ...
research
∙
12/05/2022
Learning Representations that Enable Generalization in Assistive Tasks
Recent work in sim2real has successfully enabled robots to act in physic...
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...
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...
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...
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...
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...
research
∙
02/05/2022
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning
Building assistive interfaces for controlling robots through arbitrary, ...
research
∙
01/18/2022
Inducing Structure in Reward Learning by Learning Features
Reward learning enables robots to learn adaptable behaviors from human i...
research
∙
11/18/2021
Assisted Robust Reward Design
Real-world robotic tasks require complex reward functions. When we defin...
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...
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...
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...
research
∙
06/11/2021
Policy Gradient Bayesian Robust Optimization for Imitation Learning
The difficulty in specifying rewards for many real-world problems has le...
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...
research
∙
04/23/2021
Optimal Cost Design for Model Predictive Control
Many robotics domains use some form of nonconvex model predictive contro...
research
∙
04/17/2021
Agnostic learning with unknown utilities
Traditional learning approaches for classification implicitly assume tha...
research
∙
04/14/2021
Situational Confidence Assistance for Lifelong Shared Autonomy
Shared autonomy enables robots to infer user intent and assist in accomp...
research
∙
03/13/2021
Dynamically Switching Human Prediction Models for Efficient Planning
As environments involving both robots and humans become increasingly com...
research
∙
03/09/2021
Analyzing Human Models that Adapt Online
Predictive human models often need to adapt their parameters online from...
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...
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...
research
∙
06/23/2020
Feature Expansive Reward Learning: Rethinking Human Input
In collaborative human-robot scenarios, when a person is not satisfied w...
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...
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...
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 ...
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...
research
∙
11/06/2019
Nonverbal Robot Feedback for Human Teachers
Robots can learn preferences from human demonstrations, but their succes...
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...
research
∙
09/22/2019
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets
Autonomous robots often encounter challenging situations where their con...
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...
research
∙
07/27/2019
Bayesian Robustness: A Nonasymptotic Viewpoint
We study the problem of robustly estimating the posterior distribution f...
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...
research
∙
06/06/2019
An Extensible Interactive Interface for Agent Design
In artificial intelligence, we often specify tasks through a reward func...
research
∙
05/27/2019
SQIL: Imitation Learning via Regularized Behavioral Cloning
Learning to imitate expert behavior given action demonstrations containi...
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 ...
research
∙
12/21/2018
Human-AI Learning Performance in Multi-Armed Bandits
People frequently face challenging decision-making problems in which out...
research
∙
12/04/2018
Learning from Intended Corrections
Our goal is to enable robots to learn cost functions from user guidance....
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...
research
∙
10/18/2018
Establishing Appropriate Trust via Critical States
In order to effectively interact with or supervise a robot, humans need ...
research
∙
10/18/2018
Expressing Robot Incapability
Our goal is to enable robots to express their incapability, and to do so...
research
∙
10/13/2018
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
The actions of an autonomous vehicle on the road affect and are affected...
research
∙
10/11/2018
Learning under Misspecified Objective Spaces
Learning robot objective functions from human input has become increasin...
research
∙
09/01/2018
Cost Functions for Robot Motion Style
We focus on autonomously generating robot motion for day to day physical...
research
∙
08/25/2018
