Pieter Abbeel

Researcher Next ID · RN-022783

Researcher · Computer Science

Seattle, Australia

Not currently recruitingFunding unknown

Research interests

Computer Science

Engineering

Reinforcement Learning in Robotics

Robot Manipulation and Learning

Domain Adaptation and Few-Shot Learning

Multimodal Machine Learning Applications

Human Pose and Action Recognition

Denoising Diffusion Probabilistic Models
arXiv (Cornell University) · 2020 · https://doi.org/10.48550/arxiv.2006.11239
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
arXiv (Cornell University) · 2018 · https://doi.org/10.48550/arxiv.1801.01290
The limits and potentials of deep learning for robotics
QUT ePrints (Queensland University of Technology) · 2018
Domain randomization for transferring deep neural networks from simulation to the real world
Journal · 2017 · https://doi.org/10.1109/iros.2017.8202133
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
arXiv (Cornell University) · 2017 · https://doi.org/10.48550/arxiv.1703.03400
VIME: Variational Information Maximizing Exploration
arXiv (Cornell University) · 2016 · https://doi.org/10.48550/arxiv.1605.09674
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
arXiv (Cornell University) · 2016 · https://doi.org/10.48550/arxiv.1611.04717
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement\n Learning
arXiv (Cornell University) · 2016 · https://doi.org/10.48550/arxiv.1611.04717
VIME: Variational Information Maximizing Exploration
Ghent University Academic Bibliography (Ghent University) · 2016

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