Ph.D. Dissertations - Pieter Abbeel

Exploration and Safety in Deep Reinforcement Learning
Joshua Achiam [2021]

How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World
Abhishek Gupta [2021]

Learning, Planning, and Acting with Models
Thanard Kurutach [2021]

Compositionality and Modularity for Robot Learning
Coline Devin [2020]

Deep Generative Models: Imitation Learning, Image Synthesis, and Compression
Jonathan Ho [2020]

Extracting and Using Preference Information from the State of the World
Rohin Shah [2020]

Mobile Robot Learning
Gregory Kahn [2020]

Overcoming Model-Bias in Reinforcement Learning
Ignasi Clavera Gilaberte [2020]

What Supervision Scales? Practical Learning Through Interaction
Carlos Florensa Campo [2020]

Optimizing for Robot Transparency
Sandy Huang [2019]

Real-World Robotic Perception and Control Using Synthetic Data
Joshua Tobin [2019]

The Sparse Manifold Transform and Unsupervised Learning for Signal Representation
Yubei Chen [2019]

Visual Dynamics Models for Robotic Planning and Control
Alex Lee [2019]

Acquiring Diverse Robot Skills via Maximum Entropy Deep Reinforcement Learning
Tuomas Haarnoja [2018]

Learning to Learn with Gradients
Chelsea Finn [2018]

Meta Learning for Control
Rocky Duan [2017]

Benchmarks for Cloud Robotics
Arjun Singh [2016]

Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
John Schulman [2016]

Machine Learning and Optimization for Neural Circuit Reconstruction
Jeremy Maitin-Shepard [2015]

Large-Scale, Low-Latency State Estimation Of Cyber- physical Systems With An Application To Traffic Estimation
Timothy Hunter [2014]

Safety, Risk Awareness and Exploration in Reinforcement Learning
Teodor Moldovan [2014]

A hybrid approach of physical laws and data-driven modeling for estimation: the example of queuing networks
Aude Hofleitner [2013]

Aircraft Design Optimization as a Geometric Program
Warren Hoburg [2013]