Legendaryaaa

Legendaryaaa's Stars

• XinJingHao/DRL-Pytorch

  Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

  Language:Python2.1k261

• sweetice/Deep-reinforcement-learning-with-pytorch

  PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

  Language:Python4.2k874

• Lizhi-sjtu/DRL-code-pytorch

  Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

  Language:Python1.2k189

• thu-ml/tianshou

  An elegant PyTorch deep reinforcement learning library.

  Language:Python8.3k1.1k

• MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning

  This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

  Language:MATLAB7.7k801

• takuseno/d3rlpy

  An offline deep reinforcement learning library

  Language:Python1.4k245

• linchangyi1/Awesome-Touch

  Tactile Sensing and Simulation; Visual Tactile Manipulation; Open Source.

  31630

• carlosferrazza/M3L

  The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning

  Language:Python232

• Genesis-Embodied-AI/DiffTactile

  [ICLR 2024] DiffTactile: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation

  Language:Python23115

• ac-93/tactile_gym

  Suite of PyBullet reinforcement learning environments targeted towards using tactile data as the main form of observation.

  Language:Python14522

• rpl-cmu/normalflow

  Language:Python21

• carolinahiguera/NCF

  Language:Python331

• MMintLab/VIRDO

  Github repository of a Visio-tactile Implicit Representations of Deformable Objects (ICRA 2022)

  Language:Jupyter Notebook211

• YizhouChen7045/Visuo-Tactile-Transformers-for-Manipulation

  For Visuo-Tactile Transformers for Manipulation

  Language:Python277

• Dou-Yiming/TaRF

  [CVPR 2024] Official repository for "Tactile-Augmented Radiance Fields".

  Language:Python551

• unishuai/softSim

  软体仿真项目

  Language:Python3

• yanx27/PointASNL

  PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

  Language:Python26234

• CVMI-Lab/PAConv

  (CVPR 2021) PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

  Language:Python29442

• davideferrari95/admittance_controller

  Admittance Control on a 6-DOF Manipulator using a FT Sensor

  Language:C++186

• brektrou/rtl8821CU

  Realtek RTL8811CU/RTL8821CU USB Wi-Fi adapter driver for Linux

  Language:C1.6k468

• antoalli/L2G

  End-to-End Learning to Grasp from Object Point Clouds

  Language:Python347

• nbfigueroa/robot_admittance_controller

  Admittance control law to generate desired motion of an end-effector (twist), given a desired or external wrench for a velocity-controlled robot.

  Language:C++7517

• tsinghua-rll/Visual-Tactile_Dataset

  A novel visual-tactile dataset for robotic manipulation

  4617

• facebookresearch/3D-Vision-and-Touch

  When told to understand the shape of a new object, the most instinctual approach is to pick it up and inspect it with your hand and eyes in tandem. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines, especially when the object is occluded by the hand touching it; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) reconstruction quality boosts with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

  Language:Python6914

• Chunpeng19/ur5e_admittance_control

  Language:Python71

• Alvin9999/new-pac

  翻墙-科学上网、自由上网、免费科学上网、免费翻墙、fanqiang、油管youtube/视频下载、软件、VPN、一键翻墙浏览器，vps一键搭建翻墙服务器脚本/教程，免费shadowsocks/ss/ssr/v2ray/goflyway账号/节点，翻墙梯子，电脑、手机、iOS、安卓、windows、Mac、Linux、路由器翻墙、科学上网、youtube视频下载、youtube油管镜像/免翻墙网站、美区apple id共享账号、翻墙-科学上网-梯子

  Language:Python59.6k9.8k

• Hymwgk/PointNetGPD

  PointNetGPD实验复现与注释

  Language:Python8422

• pyni/PointNet2_Grasping_Data_Part

  The dataset of our paper for ICRA 2020

  Language:Python566

• kaixindelele/ChatPaper

  Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

  Language:Python18.8k1.9k

• liuchunsense/AFFGA-Net

  Language:Python5517