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07/13/2021
Motion Planning by Learning the Solution Manifold in Trajectory Optimization
The objective function used in trajectory optimization is often non-conv...
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03/12/2021
Discovering Diverse Solutions in Deep Reinforcement Learning
Reinforcement learning (RL) algorithms are typically limited to learning...
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07/24/2020
Learning the Solution Manifold in Optimization and Its Application in Motion Planning
Optimization is an essential component for solving problems in wide-rang...
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06/04/2020
Meta-Model-Based Meta-Policy Optimization
Model-based reinforcement learning (MBRL) has been applied to meta-learn...
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03/16/2020
Multimodal Trajectory Optimization for Motion Planning
Existing motion planning methods often have two drawbacks: 1) goal confi...
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12/09/2019
Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
Imitation learning is an intuitive approach for teaching motion to robot...
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10/03/2019
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Many real world tasks require multiple agents to work together. Multi-ag...
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01/05/2019
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Real-world tasks are often highly structured. Hierarchical reinforcement...
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11/16/2018
An Algorithmic Perspective on Imitation Learning
As robots and other intelligent agents move from simple environments and...
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11/28/2017