Ching-An Cheng

research

∙ 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...

0 Vivek Myers, et al. ∙

research

∙ 06/05/2023

Survival Instinct in Offline Reinforcement Learning

We present a novel observation about the behavior of offline reinforceme...

0 Anqi Li, et al. ∙

research

∙ 06/01/2023

Improving Offline RL by Blending Heuristics

We propose Heuristic Blending (HUBL), a simple performance-improving tec...

0 Sinong Geng, et al. ∙

research

∙ 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...

0 Anqi Li, et al. ∙

research

∙ 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...

0 Garrett Thomas, et al. ∙

research

∙ 02/21/2023

Adversarial Model for Offline Reinforcement Learning

We propose a novel model-based offline Reinforcement Learning (RL) frame...

0 Mohak Bhardwaj, et al. ∙

research

∙ 01/06/2023

Provable Reset-free Reinforcement Learning by No-Regret Reduction

Real-world reinforcement learning (RL) is often severely limited since t...

0 Hoai-An Nguyen, et al. ∙

research

∙ 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...

0 Tengyang Xie, et al. ∙

research

∙ 08/15/2022

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

Simulated humanoids are an appealing research domain due to their physic...

0 Nolan Wagener, et al. ∙

research

∙ 07/13/2022

Hindsight Learning for MDPs with Exogenous Inputs

We develop a reinforcement learning (RL) framework for applications that...

0 Sean R. Sinclair, et al. ∙

research

∙ 06/01/2022

Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation

We study lifelong reinforcement learning (RL) in a regret minimization s...

0 Sanae Amani, et al. ∙

research

∙ 02/05/2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning

We propose Adversarially Trained Actor Critic (ATAC), a new model-free a...

0 Ching-An Cheng, et al. ∙

research

∙ 06/16/2021

Safe Reinforcement Learning Using Advantage-Based Intervention

Many sequential decision problems involve finding a policy that maximize...

0 Nolan Wagener, et al. ∙

research

∙ 06/13/2021

Bellman-consistent Pessimism for Offline Reinforcement Learning

The use of pessimism, when reasoning about datasets lacking exhaustive e...

0 Tengyang Xie, et al. ∙

research

∙ 06/05/2021

Heuristic-Guided Reinforcement Learning

We provide a framework for accelerating reinforcement learning (RL) algo...

0 Ching-An Cheng, et al. ∙

research

∙ 03/24/2021

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation

Policy optimization methods are popular reinforcement learning algorithm...

0 Andrea Zanette, et al. ∙

research

∙ 03/10/2021

RMP2: A Structured Composable Policy Class for Robot Learning

We consider the problem of learning motion policies for acceleration-bas...

0 Anqi Li, et al. ∙

research

∙ 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...

0 Ching-An Cheng, et al. ∙

research

∙ 07/06/2020

Explaining Fast Improvement in Online Policy Optimization

Online policy optimization (OPO) views policy optimization for sequentia...

0 Xinyan Yan, et al. ∙

research

∙ 07/01/2020

Policy Improvement from Multiple Experts

Despite its promise, reinforcement learning's real-world adoption has be...

0 Ching-An Cheng, et al. ∙

research

∙ 03/15/2020

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Predicting calibrated confidence scores for multi-class deep networks is...

5 Amir Rahimi, et al. ∙

research

∙ 12/03/2019

Continuous Online Learning and New Insights to Online Imitation Learning

Online learning is a powerful tool for analyzing iterative algorithms. H...

33 Jonathan Lee, et al. ∙

research

∙ 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...

0 Ching-An Cheng, et al. ∙

research

∙ 10/07/2019

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

RMPflow is a recently proposed policy-fusion framework based on differen...

9 Mustafa Mukadam, et al. ∙

research

∙ 08/08/2019

Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

Policy gradient methods have demonstrated success in reinforcement learn...

0 Ching-An Cheng, et al. ∙

research

∙ 03/29/2019

Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks

Robotic systems often need to consider multiple tasks concurrently. This...

0 Anqi Li, et al. ∙

research

∙ 02/24/2019

An Online Learning Approach to Model Predictive Control

Model predictive control (MPC) is a powerful technique for solving dynam...

0 Nolan Wagener, et al. ∙

research

∙ 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, ...

8 Ching-An Cheng, et al. ∙

research

∙ 11/16/2018

RMPflow: A Computational Graph for Automatic Motion Policy Generation

We develop a novel policy synthesis algorithm, RMPflow, based on geometr...

0 Ching-An Cheng, et al. ∙

research

∙ 10/25/2018

Truncated Back-propagation for Bilevel Optimization

Bilevel optimization has been recently revisited for designing and analy...

0 Amirreza Shaban, et al. ∙

research

∙ 10/15/2018

Predictor-Corrector Policy Optimization

We present a predictor-corrector framework, called PicCoLO, that can tra...

0 Ching-An Cheng, et al. ∙

research

∙ 09/24/2018

Orthogonally Decoupled Variational Gaussian Processes

Gaussian processes (GPs) provide a powerful non-parametric framework for...

0 Hugh Salimbeni, et al. ∙

research

∙ 06/12/2018

Model-Based Imitation Learning with Accelerated Convergence

Sample efficiency is critical in solving real-world reinforcement learni...

0 Ching-An Cheng, et al. ∙

research

∙ 05/26/2018

Fast Policy Learning through Imitation and Reinforcement

Imitation learning (IL) consists of a set of tools that leverage expert ...

0 Ching-An Cheng, et al. ∙

research

∙ 01/22/2018

Convergence of Value Aggregation for Imitation Learning

Value aggregation is a general framework for solving imitation learning ...

0 Ching-An Cheng, et al. ∙

research

∙ 11/28/2017

Variational Inference for Gaussian Process Models with Linear Complexity

Large-scale Gaussian process inference has long faced practical challeng...

0 Ching-An Cheng, et al. ∙

Ching-An Cheng

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