94 lines
2.5 KiB
Markdown
94 lines
2.5 KiB
Markdown
# Roadmap
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* Change the lookat point to teh pointcloud average xyz instead of bbox center
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* Debug with model output pose
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* Change simulation scenes
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* Interface with GS-Net
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# Updated installation steps fo my PC environment
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```sh
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# Install Active Grasp
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sudo apt install liborocos-kdl-dev
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mkdir -p ws/src && cd ws/src
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git clone https://github.com/0nhc/active_grasp.git
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conda create -n active_grasp python=3.8
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cd active_grasp && conda activate active_grasp
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pip install -r requirements.txt
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conda install libffi==3.3
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conda install conda-forge::python-orocos-kdl
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cd ..
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git clone https://github.com/0nhc/vgn.git -b devel
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cd vgn
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pip install -r requirements.txt
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cd ..
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git clone https://github.com/0nhc/robot_helpers.git
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cd ..
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rosdep install --from-paths src --ignore-src -r -y
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catkin build
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# Install Active Perception
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cd <path-to-your-ws>/src/active_grasp/src/active_grasp/active_perception/modules/module_lib/pointnet2_utils/pointnet2
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pip install -e .
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```
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# Updated Features
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* Added our baseline: src/active_grasp/active_perception_policy.py
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* Added RGB and Segmentation image publishers. The segmentation ID 1 corresponds to the grasping target object.
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# Closed-Loop Next-Best-View Planning for Target-Driven Grasping
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This repository contains the implementation of our IROS 2022 submission, _"Closed-Loop Next-Best-View Planning for Target-Driven Grasping"_. [[Paper](http://arxiv.org/abs/2207.10543)][[Video](https://youtu.be/67W_VbSsAMQ)]
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## Setup
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The experiments were conducted with a Franka Emika Panda arm and a Realsense D435 attached to the wrist of the robot. The code was developed and tested on Ubuntu 20.04 with ROS Noetic. It depends on the following external packages:
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- [MoveIt](https://github.com/ros-planning/panda_moveit_config)
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- [robot_helpers](https://github.com/mbreyer/robot_helpers)
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- [TRAC-IK](http://wiki.ros.org/trac_ik)
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- [VGN](https://github.com/ethz-asl/vgn/tree/devel)
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- franka_ros and realsense2_camera (only required for hardware experiments)
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Additional Python dependencies can be installed with
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```
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pip install -r requirements.txt
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```
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Run `catkin build active_grasp` to build the package.
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Finally, download the [assets folder](https://drive.google.com/file/d/1xJF9Cd82ybCH3nCdXtQRktTr4swDcNFD/view) and extract it inside the repository.
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## Experiments
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Start a roscore.
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```
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roscore
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```
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To run simulation experiments.
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```
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roslaunch active_grasp env.launch sim:=true
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python3 scripts/run.py nbv
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```
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To run real-world experiments.
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```
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roslaunch active_grasp hw.launch
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roslaunch active_grasp env.launch sim:=false
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python3 scripts/run.py nbv --wait-for-input
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```
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