PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

Overview

简体中文 | English

PaddleRobotics

paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。

人机交互

主动多模交互技术TFVT-HRI

主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人进行决策、输出表情、动作、声音等响应。

复杂运动控制

四足机器人

通过强化学习,实现四足机器人的行走、避障、越障等功能。

环境感知

通过视觉、雷达、超声波、红外等传感器实时获取当前环境信息,进行数据融合处理,精确判断环境状态。

Slam定位导航

通过感知的环境信息,经算法处理后实现当前可通行区域的判断和多目标跟踪。

许可证书

本项目的发布受Apache 2.0 license许可认证。

如何贡献代码

我们非常欢迎你可以为PaddleRobotics提供代码,也十分感谢你的反馈。

You might also like...
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

(CVPR 2022) A minimalistic mapless end-to-end stack for joint perception, prediction, planning and control for self driving.
(CVPR 2022) A minimalistic mapless end-to-end stack for joint perception, prediction, planning and control for self driving.

LAV Learning from All Vehicles Dian Chen, Philipp Krähenbühl CVPR 2022 (also arXiV 2203.11934) This repo contains code for paper Learning from all veh

Repo for CVPR2021 paper
Repo for CVPR2021 paper "QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information"

QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information by Masato Tamura, Hiroki Ohashi, and Tomoaki Yosh

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation A pytorch-version implementation codes of paper:

This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection
This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

Semantic SLAM This package can perform optimization of pose estimated from VO/VIO methods which tend to drift over time. It uses planar surfaces extra

[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python

Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python THIS PROJECT IS CURRENTLY A WORK IN PROGRESS AND THUS THIS REPOSITORY I

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Product-based-recommendation-system - A product based recommendation system which uses Machine learning algorithm such as KNN and cosine similarity
Comments
  • HRI/TFVT_HRI : No module named 'interaction.common.discrete_uc_ctrl

    HRI/TFVT_HRI : No module named 'interaction.common.discrete_uc_ctrl

    File "HRI/TFVT_HRI/interaction/action.py", line 2, in from interaction.common.discrete_uc_ctrl import DiscreteUCController ModuleNotFoundError: No module named 'interaction.common.discrete_uc_ctrl'

    The common directory dont have the discrete_uc_ctrl.py : data.py data_via_decord.py discrete_ctrl.py init.py pycache utils.py

    can you provide it?

    thanks

    opened by tarikbeijing 1
  • ValueError: device value error, must be str,

    ValueError: device value error, must be str,

    请问一下,我只在仿真环境中跑了train.py, 按照readme的指导,但是报告了一个异常, ValueError: device value error, must be str, paddle.CPUPlace(), paddle.CUDAPlace(), paddle.CUDAPinnedPlace() or paddle.XPUPlace(), but the type of device is device 我测试了一下,device=cuda, type(decvice) = <class 'torch.device'>, 一直找不到哪里的问题,可以帮忙看一下么?

    opened by IverYangg 6
  • 运行Dynamic_train.py时报错Exception in thread Thread-2:parl.remote.exceptions.RemoteError: [PARL remote error when calling function __init__]:

    运行Dynamic_train.py时报错Exception in thread Thread-2:parl.remote.exceptions.RemoteError: [PARL remote error when calling function __init__]:

    作者您好,我运行QuadrupedalRobots/ETGRL/train.py训练是没有问题的,但是运行Dynamic_train.py时出现了下面3个问题,查看Dynamic_train.py同级目录里是有./model/Dynamic_parallel_model.py文件的,请问是什么原因造成的呢?

    Exception in thread Thread-2: parl.remote.exceptions.RemoteError: [PARL remote error when calling function __init__]:

    [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py' FileNotFoundError: [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py'

    parl.remote.exceptions.FutureFunctionError: There is an error raised when calling the future function __init__.

    完整报错信息

    [04-26 20:29:14 MainThread @Dynamic_train.py:71] args:Namespace(K=20, alg='ga', eval=0, gamma=1, load='', outdir='Dynamic', sigma=0.1, steps=10000, suffix='exp0', thread=2, xparl='192.168.30.145:8037') Exception in thread Thread-5: Traceback (most recent call last): File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/threading.py", line 932, in _bootstrap_inner self.run() File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/future_mode/proxy_wrapper_nowait.py", line 92, in _run_object_in_backend raise e File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/future_mode/proxy_wrapper_nowait.py", line 82, in _run_object_in_backend self._xparl_remote_wrapper_obj = remote_wrapper( File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/remote_wrapper.py", line 107, in init raise RemoteError('init', traceback_str) parl.remote.exceptions.RemoteError: [PARL remote error when calling function __init__]: [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py' traceback: Traceback (most recent call last): File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/job.py", line 297, in wait_for_connection cls = load_remote_class(message[1]) File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/remote_class_serialization.py", line 207, in load_remote_class with open(file_name + '.py') as t_file: FileNotFoundError: [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py'

    Exception in thread Thread-4: Traceback (most recent call last): File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/threading.py", line 932, in _bootstrap_inner self.run() File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/future_mode/proxy_wrapper_nowait.py", line 92, in _run_object_in_backend raise e File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/future_mode/proxy_wrapper_nowait.py", line 82, in _run_object_in_backend self._xparl_remote_wrapper_obj = remote_wrapper( File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/remote_wrapper.py", line 107, in init raise RemoteError('init', traceback_str) parl.remote.exceptions.RemoteError: [PARL remote error when calling function __init__]: [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py' traceback: Traceback (most recent call last): File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/job.py", line 297, in wait_for_connection cls = load_remote_class(message[1]) File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/remote_class_serialization.py", line 207, in load_remote_class with open(file_name + '.py') as t_file: FileNotFoundError: [Errno 2] No such file or directory: './model/Dynamic_parallel_model.py'

    Traceback (most recent call last): File "/mnt/hgfs/虚拟机/PaddleRobotics-main/QuadrupedalRobots/ETGRL/Dynamic_train.py", line 74, in main() File "/mnt/hgfs/虚拟机/PaddleRobotics-main/QuadrupedalRobots/ETGRL/Dynamic_train.py", line 72, in main model.train(args.steps) File "/mnt/hgfs/虚拟机/PaddleRobotics-main/QuadrupedalRobots/ETGRL/model/Dynamic_parallel_model.py", line 159, in train mean_re = self.update(epoch) File "/mnt/hgfs/虚拟机/PaddleRobotics-main/QuadrupedalRobots/ETGRL/model/Dynamic_parallel_model.py", line 128, in update future_objects.append(self.agent_list[i].batch_sample_episodes(param=solutions[i*self.K:(i+1)*self.K,:],K = self.K)) File "/home/senweihuang/anaconda3/envs/parl/lib/python3.8/site-packages/parl/remote/future_mode/proxy_wrapper_nowait.py", line 144, in getattr raise self._xparl_remote_object_exception parl.remote.exceptions.FutureFunctionError: There is an error raised when calling the future function __init__. You can see the detailed error message above, which is printed by another thread.

    Process finished with exit code 1

    环境

    Ubuntu 18.04 python 3.8 parl = 1.4.0 torch = 1.7.0 rlschool = 1.0.2

    @xueeinstein

    opened by Senwei-Huang 5
  • Dynamic_train时加载data/dynamic/内的数据含义请教

    Dynamic_train时加载data/dynamic/内的数据含义请教

    作者您好,我在研读了你们发表的论文后想跑一下这个项目,但是在看域自适应训练代码时遇到一些模糊的地方,想请教一下。

    在运行Dynamic_train.py时,下面这行代码: MEAN_INFO = np.load("data/dynamic/mean_dict_5_18.npz") # data used for dynamic adaptation 加载进来的数据的键分别代表什么意思呢? dict_keys(['exp_motor_mean', 'exp_motor_std', 'exp_drpy_mean', 'exp_drpy_std', 'ori_motor_mean', 'ori_motor_std', 'ori_drpy_mean', 'ori_drpy_std', 'height_motor_mean', 'height_motor_std', 'height_drpy_mean', 'height_drpy_std'])

    下面代码:

    GAIT_LIST["exp"] = np.load("data/dynamic/gait_action_list_t0.3.npy")
    GAIT_LIST["ori"] = np.load("data/dynamic/gait_action_list_CPG_ori.npy")
    GAIT_LIST["height"] = np.load("data/dynamic/gait_action_list_CPG_height.npy")
    

    加载进来的是什么数据呢?不知道您是否方便公开一下,谢谢

    @xueeinstein

    opened by Senwei-Huang 7
Owner
null
MohammadReza Sharifi 27 Dec 13, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

yifan liu 147 Dec 3, 2022
Synthesizing Long-Term 3D Human Motion and Interaction in 3D in CVPR2021

Long-term-Motion-in-3D-Scenes This is an implementation of the CVPR'21 paper "Synthesizing Long-Term 3D Human Motion and Interaction in 3D". Please ch

Jiashun Wang 76 Dec 13, 2022
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation [Project website] [Paper] This project is a PyTorch i

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 6 Feb 28, 2022
Autonomous Perception: 3D Object Detection with Complex-YOLO

Autonomous Perception: 3D Object Detection with Complex-YOLO LiDAR object detect

Thomas Dunlap 2 Feb 18, 2022
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance This is the codebase for video-based human motion reconstruction in human-mot

Jiachen Xu 5 Jul 14, 2022
Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator

DRL-robot-navigation Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gra

null 87 Jan 7, 2023
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 3, 2023
Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle.

Paddle-Adversarial-Toolbox Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle. Model Zoo Common FGS

AgentMaker 17 Nov 8, 2022