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Jesse Zhang's Projects

3d-diffusion-policy icon 3d-diffusion-policy

[RSS 2024] 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

3d_diffuser_actor icon 3d_diffuser_actor

Code for the paper "3D Diffuser Actor: Policy Diffusion with 3D Scene Representations"

alf icon alf

Agent Learning Framework https://alf.readthedocs.io

alfred icon alfred

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

awesome-llm-robotics icon awesome-llm-robotics

A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites

awesome-program icon awesome-program

A curated list of papers related to program synthesis, program induction, program execution, program and code repair, and programmatic reinforcement learning.

carl icon carl

Github Repo for CARL: Cautious Adaptation for RL in Safety Critical Settings

causalskilllearning icon causalskilllearning

Codebase for project about unsupervised skill learning via variational inference and causality.

dm_control icon dm_control

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

e-mission-eval-public-data icon e-mission-eval-public-data

Repository for scripts to evaluate the e-mission platform using public datasets. Structured to be compatible with binder

e.t. icon e.t.

Episodic Transformer (E.T.) is a novel attention-based architecture for vision-and-language navigation. E.T. is based on a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions.

h-baselines icon h-baselines

A repository of high-performing hierarchical reinforcement learning models and algorithms.

handful-of-trials-pytorch icon handful-of-trials-pytorch

Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

hidio icon hidio

Github repo for HIDIO: Hierarchical Reinforcement Learning by Discovering Intrinsic Options

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