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06/30/2023
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Our goal is for robots to follow natural language instructions like "put...
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06/05/2023
Survival Instinct in Offline Reinforcement Learning
We present a novel observation about the behavior of offline reinforceme...
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06/01/2023
Improving Offline RL by Blending Heuristics
We propose Heuristic Blending (HUBL), a simple performance-improving tec...
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03/30/2023
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
We study a new paradigm for sequential decision making, called offline P...
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03/15/2023
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining
A rich representation is key to general robotic manipulation, but existi...
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02/21/2023
Adversarial Model for Offline Reinforcement Learning
We propose a novel model-based offline Reinforcement Learning (RL) frame...
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01/06/2023
Provable Reset-free Reinforcement Learning by No-Regret Reduction
Real-world reinforcement learning (RL) is often severely limited since t...
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11/08/2022
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data
We propose a new model-based offline RL framework, called Adversarial Mo...
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08/15/2022
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
Simulated humanoids are an appealing research domain due to their physic...
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07/13/2022
Hindsight Learning for MDPs with Exogenous Inputs
We develop a reinforcement learning (RL) framework for applications that...
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06/01/2022
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
We study lifelong reinforcement learning (RL) in a regret minimization s...
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02/05/2022
Adversarially Trained Actor Critic for Offline Reinforcement Learning
We propose Adversarially Trained Actor Critic (ATAC), a new model-free a...
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06/16/2021
Safe Reinforcement Learning Using Advantage-Based Intervention
Many sequential decision problems involve finding a policy that maximize...
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06/13/2021
Bellman-consistent Pessimism for Offline Reinforcement Learning
The use of pessimism, when reasoning about datasets lacking exhaustive e...
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06/05/2021
Heuristic-Guided Reinforcement Learning
We provide a framework for accelerating reinforcement learning (RL) algo...
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03/24/2021
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Policy optimization methods are popular reinforcement learning algorithm...
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03/10/2021
RMP2: A Structured Composable Policy Class for Robot Learning
We consider the problem of learning motion policies for acceleration-bas...
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07/25/2020
RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies
Generating robot motion for multiple tasks in dynamic environments is ch...
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07/06/2020
Explaining Fast Improvement in Online Policy Optimization
Online policy optimization (OPO) views policy optimization for sequentia...
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07/01/2020
Policy Improvement from Multiple Experts
Despite its promise, reinforcement learning's real-world adoption has be...
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03/15/2020
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Predicting calibrated confidence scores for multi-class deep networks is...
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12/03/2019
Continuous Online Learning and New Insights to Online Imitation Learning
Online learning is a powerful tool for analyzing iterative algorithms. H...
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11/14/2019
A Reduction from Reinforcement Learning to No-Regret Online Learning
We present a reduction from reinforcement learning (RL) to no-regret onl...
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10/07/2019
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
RMPflow is a recently proposed policy-fusion framework based on differen...
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08/08/2019
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
Policy gradient methods have demonstrated success in reinforcement learn...
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03/29/2019
Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks
Robotic systems often need to consider multiple tasks concurrently. This...
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02/24/2019
An Online Learning Approach to Model Predictive Control
Model predictive control (MPC) is a powerful technique for solving dynam...
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02/19/2019
Online Learning with Continuous Variations: Dynamic Regret and Reductions
We study the dynamic regret of a new class of online learning problems, ...
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11/16/2018
RMPflow: A Computational Graph for Automatic Motion Policy Generation
We develop a novel policy synthesis algorithm, RMPflow, based on geometr...
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10/25/2018
Truncated Back-propagation for Bilevel Optimization
Bilevel optimization has been recently revisited for designing and analy...
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10/15/2018
Predictor-Corrector Policy Optimization
We present a predictor-corrector framework, called PicCoLO, that can tra...
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09/24/2018
Orthogonally Decoupled Variational Gaussian Processes
Gaussian processes (GPs) provide a powerful non-parametric framework for...
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06/12/2018
Model-Based Imitation Learning with Accelerated Convergence
Sample efficiency is critical in solving real-world reinforcement learni...
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05/26/2018
Fast Policy Learning through Imitation and Reinforcement
Imitation learning (IL) consists of a set of tools that leverage expert ...
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01/22/2018
Convergence of Value Aggregation for Imitation Learning
Value aggregation is a general framework for solving imitation learning ...
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11/28/2017
