Abbeel

About

Professor Pieter Abbeel is the Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab at UC Berkeley. His research focuses on building increasingly intelligent systems, pushing the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn. He also studies the influence of AI on society and investigates how AI can advance other science and engineering disciplines. Abbeel's work includes developing foundational AI classes that have been taken by over 100,000 students through edX, and his materials on Deep Reinforcement Learning and Deep Unsupervised Learning are considered standard references for AI researchers. He has founded three companies—Gradescope, Covariant, and Berkeley Open Arms—and advises numerous AI and robotics startups. Recognized with multiple awards including the PECASE, NSF-CAREER, and DARPA-YFA, his work is frequently featured in major press outlets. His educational background includes a Ph.D. in Computer Science from Stanford University and an M.S. in Electrical Engineering from KU Leuven, Belgium.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Mathematics
• Engineering
• Algorithm
• Programming language
• Applied mathematics
• Electrical engineering
• Computer graphics (images)

Selected publications

• Denoising Diffusion Probabilistic Models

  arXiv (Cornell University) · 2020 · 5608 citations

  Senior authorCorresponding

  DiffuCpG 1. Introduction In this study, we used a generative AI diffusion model to address missing methylation data. We trained the model with Whole-Genome Bisulfite Sequencing data from 26 acute myeloid leukemia samples and validated it with Reduced Representation Bisulfite Sequencing data from 93 myelodysplastic syndrome and 13 normal samples. Additional testing included data from the Illumina 450k methylation array and Single-Cell Reduced Representation Bisulfite Sequencing on HepG2 cells. Ou…

  DOI

• Decision Transformer: Reinforcement Learning via Sequence Modeling

  arXiv (Cornell University) · 2021 · 465 citations

  We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply…

  DOI

• Reinforcement Learning with Augmented Data

  arXiv (Cornell University) · 2020 · 246 citations

  Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learning and (b) generalization to new environments. To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform th…

  DOI

• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

  2021 · 197 citations

  Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling; any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-to-real transfer, dom…

  DOI

• Learning to Manipulate Deformable Objects without Demonstrations

  2020 · 164 citations

  Senior authorCorresponding

  In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning. First, we propose an iterative pick-place action space that encodes the conditional relationship between picking and placing on deformable objects. The explicit structural encoding enables faster learning under complex object dynamics. Second, instead of jointly learnin…

  DOI

Frequent coauthors

• Sergey Levine

  207 shared

• Takayuki Osa

  The University of Tokyo

  66 shared

• J. Andrew Bagnell

  65 shared

• Joni Pajarinen

  65 shared

• Gerhard Neumann

  65 shared

• Jan Peters

  Technical University of Darmstadt

  65 shared

• Aviv Tamar

  Technion – Israel Institute of Technology

  56 shared

• Kimin Lee

  55 shared

Education

• Ph.D., Electrical Engineering and Computer Sciences

  University of California, Berkeley

  2008

• M.S., Electrical Engineering and Computer Sciences

  University of California, Berkeley

  2003

• B.S., Electrical Engineering and Computer Sciences

  University of California, Berkeley

  2001

Awards & honors

• PECASE
• NSF-CAREER
• ONR-YIP
• Darpa-YFA
• TR35