Lerrel Pinto

Welcome!

I am an Assistant Professor of Computer Science at NYU Courant working on problems in Robotics and Machine Learning. I am also affiliated with the Center for Data Science. Together with several wonderful colleagues I am part of the CILVR (Computational Intelligence, Learning, Vision and Robotics) group.

My current goal is to get robots to generalize and adapt in the diverse world we live in. To this end, my research touches the areas of Robot Learning, Representation Learning, Reinforcement Learning, and Affordable Robotics.

News

Recent Talks

Here are some public talks that covers my recent research:

Courses Taught at NYU

  • (Spring 2021) CSCI-UA 74 Big Ideas in Artificial Intelligence.

    • I’m teaching the lecture on Robotics
  • (Fall 2020) CSCI-GA 3033-090 Special Topics: Deep Reinforcement Learning.

Selected Research and Publications


Reinforcement Learning with Prototypical Representations
Reinforcement Learning with Prototypical Representations

Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency

Self-Supervised Policy Adaptation during Deployment
Self-Supervised Policy Adaptation during Deployment

Task-Agnostic Morphology Evolution
Task-Agnostic Morphology Evolution

Visual Imitation Made Easy
Visual Imitation Made Easy

Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

Robust Policies via Mid-Level Visual Representations
Robust Policies via Mid-Level Visual Representations

Automatic Curriculum Learning through Value Disagreement
Automatic Curriculum Learning through Value Disagreement

Generalized Hindsight for Reinforcement Learning
Generalized Hindsight for Reinforcement Learning

Reinforcement Learning with Augmented Data
Reinforcement Learning with Augmented Data

Learning to Manipulate Deformable Objects without Demonstrations
Learning to Manipulate Deformable Objects without Demonstrations

Swoosh! Rattle! Thump! - Actions that Sound
Swoosh! Rattle! Thump! - Actions that Sound

Hierarchically Decoupled Imitation for Morphological Transfer
Hierarchically Decoupled Imitation for Morphological Transfer

Discovering Motor Programs by Recomposing Demonstrations
Discovering Motor Programs by Recomposing Demonstrations

Robot Learning via Human Adversarial Games
Robot Learning via Human Adversarial Games

PyRobot: An Open-source Robotics Framework for Research and Benchmarking
PyRobot: An Open-source Robotics Framework for Research and Benchmarking

Environment Probing Interaction Policies
Environment Probing Interaction Policies

Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

Asymmetric Actor Critic for Image-Based Robot Learning
Asymmetric Actor Critic for Image-Based Robot Learning

CASSL: Curriculum Accelerated Self-Supervised Learning
CASSL: Curriculum Accelerated Self-Supervised Learning

Learning to Fly by Crashing
Learning to Fly by Crashing

Robust Adversarial Reinforcement Learning
Robust Adversarial Reinforcement Learning


Supervision via Competition: Robot Adversaries for Learning Tasks
Supervision via Competition: Robot Adversaries for Learning Tasks

The Curious Robot: Learning Visual Representations via Physical Interactions
The Curious Robot: Learning Visual Representations via Physical Interactions

Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours
Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours