b'Scott Niekum'

research

∙ 02/16/2023

Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods

It is well known that Reinforcement Learning (RL) can be formulated as a...

0 Harshit Sikchi, et al. ∙

research

∙ 01/24/2023

Language-guided Task Adaptation for Imitation Learning

We introduce a novel setting, wherein an agent needs to learn a task fro...

0 Prasoon Goyal, et al. ∙

research

∙ 11/01/2022

Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots

Humans use audio signals in the form of spoken language or verbal reacti...

0 Akanksha Saran, et al. ∙

research

∙ 06/05/2022

Models of human preference for learning reward functions

The utility of reinforcement learning is limited by the alignment of rew...

0 W. Bradley Knox, et al. ∙

research

∙ 06/01/2022

Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL

We introduce an offline reinforcement learning (RL) algorithm that expli...

0 Wonjoon Goo, et al. ∙

research

∙ 02/07/2022

A Ranking Game for Imitation Learning

We propose a new framework for imitation learning - treating imitation a...

6 Harshit Sikchi, et al. ∙

research

∙ 11/06/2021

SOPE: Spectrum of Off-Policy Estimators

Many sequential decision making problems are high-stakes and require off...

0 Christina J. Yuan, et al. ∙

research

∙ 10/05/2021

You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL

The goal of offline reinforcement learning (RL) is to find an optimal po...

0 Wonjoon Goo, et al. ∙

research

∙ 08/12/2021

Distributional Depth-Based Estimation of Object Articulation Models

We propose a method that efficiently learns distributions over articulat...

0 Ajinkya Jain, et al. ∙

research

∙ 06/30/2021

Robust Generative Adversarial Imitation Learning via Local Lipschitzness

We explore methodologies to improve the robustness of generative adversa...

0 Farzan Memarian, et al. ∙

research

∙ 06/05/2021

Zero-shot Task Adaptation using Natural Language

Imitation learning and instruction-following are two common approaches t...

0 Prasoon Goyal, et al. ∙

research

∙ 05/27/2021

Adversarial Intrinsic Motivation for Reinforcement Learning

Learning with an objective to minimize the mismatch with a reference dis...

12 Ishan Durugkar, et al. ∙

research

∙ 04/26/2021

Universal Off-Policy Evaluation

When faced with sequential decision-making problems, it is often useful ...

0 Yash Chandak, et al. ∙

research

∙ 03/08/2021

Self-Supervised Online Reward Shaping in Sparse-Reward Environments

We propose a novel reinforcement learning framework that performs self-s...

0 Farzan Memarian, et al. ∙

research

∙ 02/16/2021

SCAPE: Learning Stiffness Control from Augmented Position Control Experiences

We introduce a sample-efficient method for learning state-dependent stif...

0 Mincheol Kim, et al. ∙

research

∙ 12/02/2020

Value Alignment Verification

As humans interact with autonomous agents to perform increasingly compli...

0 Daniel S. Brown, et al. ∙

research

∙ 09/28/2020

The EMPATHIC Framework for Task Learning from Implicit Human Feedback

Reactions such as gestures, facial expressions, and vocalizations are an...

0 Yuchen Cui, et al. ∙

research

∙ 08/24/2020

ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory

Robots in human environments will need to interact with a wide variety o...

0 Ajinkya Jain, et al. ∙

research

∙ 07/30/2020

PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards

Reinforcement learning (RL), particularly in sparse reward settings, oft...

19 Prasoon Goyal, et al. ∙

research

∙ 07/24/2020

Bayesian Robust Optimization for Imitation Learning

One of the main challenges in imitation learning is determining what act...

23 Daniel S. Brown, et al. ∙

research

∙ 02/28/2020

Efficiently Guiding Imitation Learning Algorithms with Human Gaze

Human gaze is known to be an intention-revealing signal in human demonst...

11 Akanksha Saran, et al. ∙

research

∙ 02/21/2020

Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

Bayesian reward learning from demonstrations enables rigorous safety and...

1 Daniel S. Brown, et al. ∙

research

∙ 02/09/2020

Local Nonparametric Meta-Learning

A central goal of meta-learning is to find a learning rule that enables ...

0 Wonjoon Goo, et al. ∙

research

∙ 12/10/2019

Deep Bayesian Reward Learning from Preferences

Bayesian inverse reinforcement learning (IRL) methods are ideal for safe...

0 Daniel S. Brown, et al. ∙

research

∙ 07/21/2019

Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty

Sudden changes in the dynamics of robotic tasks, such as contact with an...

0 Ajinkya Jain, et al. ∙

research

∙ 07/16/2019

Understanding Teacher Gaze Patterns for Robot Learning

Human gaze is known to be a strong indicator of underlying human intenti...

0 Akanksha Saran, et al. ∙

research

∙ 07/09/2019

Ranking-Based Reward Extrapolation without Rankings

The performance of imitation learning is typically upper-bounded by the ...

0 Daniel S. Brown, et al. ∙

research

∙ 07/06/2019

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

A key challenge in intelligent robotics is creating robots that are capa...

0 Oliver Kroemer, et al. ∙

research

∙ 05/27/2019

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning

Deep reinforcement learning encompasses many versatile tools for designi...

0 Caleb Chuck, et al. ∙

research

∙ 05/07/2019

Uncertainty-Aware Data Aggregation for Deep Imitation Learning

Estimating statistical uncertainties allows autonomous agents to communi...

8 Yuchen Cui, et al. ∙

research

∙ 04/12/2019

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

A critical flaw of existing inverse reinforcement learning (IRL) methods...

0 Daniel S. Brown, et al. ∙

research

∙ 03/05/2019

Using Natural Language for Reward Shaping in Reinforcement Learning

Recent reinforcement learning (RL) approaches have shown strong performa...

0 Prasoon Goyal, et al. ∙

research

∙ 01/08/2019

Risk-Aware Active Inverse Reinforcement Learning

Active learning from demonstration allows a robot to query a human for s...

0 Daniel S. Brown, et al. ∙

research

∙ 11/08/2018

LAAIR: A Layered Architecture for Autonomous Interactive Robots

When developing general purpose robots, the overarching software archite...

0 Yuqian Jiang, et al. ∙

research

∙ 10/02/2018

Towards Online Learning from Corrective Demonstrations

Robots operating in real-world human environments will likely encounter ...

0 Reymundo A. Gutierrez, et al. ∙

research

∙ 06/29/2018

Learning Multi-Step Robotic Tasks from Observation

Due to burdensome data requirements, learning from demonstration often f...

0 Wonjoon Goo, et al. ∙

research

∙ 06/04/2018

Importance Sampling Policy Evaluation with an Estimated Behavior Policy

In reinforcement learning, off-policy evaluation is the task of using da...

0 Josiah Hanna, et al. ∙

research

∙ 05/20/2018

Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

Inverse reinforcement learning (IRL) infers a reward function from demon...

0 Daniel S. Brown, et al. ∙

research

∙ 02/12/2018

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

Noisy observations coupled with nonlinear dynamics pose one of the bigge...

0 Ajinkya Jain, et al. ∙

research

∙ 02/12/2018

Leveraging Task Knowledge for Robot Motion Planning Under Uncertainty

Noisy observations coupled with nonlinear dynamics pose one of the bigge...

0 Ajinkya Jain, et al. ∙

research

∙ 08/29/2017

Safe Reinforcement Learning via Shielding

Reinforcement learning algorithms discover policies that maximize reward...

0 Mohammed Alshiekh, et al. ∙

research

∙ 07/03/2017

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

In the field of reinforcement learning there has been recent progress to...

0 Daniel S. Brown, et al. ∙

research

∙ 06/12/2017

Data-Efficient Policy Evaluation Through Behavior Policy Search

We consider the task of evaluating a policy for a Markov decision proces...

0 Josiah P. Hanna, et al. ∙

research

∙ 06/20/2016

Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation

For an autonomous agent, executing a poor policy may be costly or even d...

0 Josiah P. Hanna, et al. ∙

Scott Niekum

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