Codebase for Automated Creation of Digital Cousins for Robust Policy Learning

[RSS 2024] "DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation" code repository

Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" (ICLR 2024)

Benchmarking Knowledge Transfer in Lifelong Robot Learning

"MimicPlay: Long-Horizon Imitation Learning by Watching Human Play" code repository

[CoRL 2023] This repository contains data generation and training code for Scaling Up & Distilling Down

Web Based Visualizer for Simulation Environments

Inference code for Llama models

Official code and checkpoint release for mobile robot foundation models: GNM, ViNT, and NoMaD.

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft

Official repository for "LIV: Language-Image Representations and Rewards for Robotic Control" (ICML 2023)

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.

Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)

An Open-Ended Embodied Agent with Large Language Models

Official Task Suite Implementation of ICML'23 Paper "VIMA: General Robot Manipulation with Multimodal Prompts"

Official Algorithm Implementation of ICML'23 Paper "VIMA: General Robot Manipulation with Multimodal Prompts"

Foundation Model for MineDojo

Building Open-Ended Embodied Agents with Internet-Scale Knowledge

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

A library for distributed ML training with PyTorch

RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

Hydra is a framework for elegantly configuring complex applications

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

This is a repo with improved, branded templates for Stanford Poster Sessions

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

Machine Learning and Computer Vision Engineer - Technical Interview Questions