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University of Southern California
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Public repository of GPT-fabric for cloth folding
RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Must-read papers on graph neural networks (GNN)
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Monitor Gaussian Splatting additional real-time viewable and differentiable outputs
[ECCV 2024 Oral] PointLLM: Empowering Large Language Models to Understand Point Clouds
A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.
A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
HAAS = Hierarchical Autonomous Agent Swarm - "Resistance is futile!"
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
A generative world for general-purpose robotics & embodied AI learning.
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Code for the paper Learning Visible Connectivity Dynamics for Cloth Smoothing
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
PCA-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models
AI-Generated Presets for Faithful 4K Color Style Transfer in Real Time [CVPR 2023]
PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals
An unofficial implementation of Joint Bilateral Learning for Real-time Universal photorealistic Style Transfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Research on Tabular Deep Learning: Papers & Packages
Automatic construction of a sequence of tasks from a large number of datasets.
Software that can perform photorealistic style transfer without the need of any post-processing steps.
Arbitrary Style Transfer with Style-Attentional Networks