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Grasping Trajectory Optimization with Point Clouds

We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D points on their link surfaces. The task space of a robot is represented by a point cloud that can be obtained from depth sensors. Using the point-cloud representation, goal reaching in grasping can be formulated as point matching, while collision avoidance can be efficiently achieved by querying the signed distance values of the robot points in the signed distance field of the scene points. Consequently, a constrained non-linear optimization problem is formulated to solve the joint motion and grasp planning problem. The advantage of our method is that the point-cloud representation is general to be used with any robot in any environment. We demonstrate the effectiveness of our method by conducting experiments on a tabletop scene and a shelf scene for grasping with a Fetch mobile manipulator and a Franka Panda arm.

arXiv, Project

Citation

If you find the package is useful in your research, please consider citing:

@misc{xiang2024grasping,
  title={Grasping Trajectory Optimization with Point Clouds}, 
  author={Yu Xiang and Sai Haneesh Allu and Rohith Peddi and Tyler Summers and Vibhav Gogate},
  year={2024},
  eprint={2403.05466},
  archivePrefix={arXiv},
  primaryClass={cs.RO}}

Installation

# create conda environment
conda create -n gto python=3.9

# activate the environment
conda activate gto

# install dependencies
pip install -r requirement.txt

Example Usage

  1. Download the SceneReplica data from here. Unzip it to the root directory of this project.

  2. Run GTO planning with SceneReplica in PyBullet. A result json file wil be saved.

    cd examples/
python pybullet_gto_planning.py --robot panda --scene_type tabletop --data_dir ../scenereplica_data

    The command line args:

  • --robot : From {"fetch", "panda"}
  • --scene_type : From {"tabletop", "shelf"}
  • --data_dir : The path for the SceneReplica data directory
  1. Experimental evaluation using a result json file 
    cd examples/
python pybullet_evaluate_plans.py --robot fetch --scene_type tabletop --data_dir ../scenereplica_data --file results_iros2024/GTO_scenereplica_fetch_tabletop_24-02-06_T181818.json
    The command line args:
  • --robot : From {"fetch", "panda"}
  • --scene_type : From {"tabletop", "shelf"}
  • --data_dir : The path for the SceneReplica data directory
  • --moblie: evaluation for mobile manipulation

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PyBullet Tabletop Example

cd examples/
python pybullet_gto_planning.py --robot fetch --scene_type tabletop --data_dir ../scenereplica_data
python pybullet_gto_planning.py --robot panda --scene_type tabletop --data_dir ../scenereplica_data

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PyBullet Shelf Example

cd examples/
python pybullet_gto_planning.py --robot fetch --scene_type shelf --data_dir ../scenereplica_data
python pybullet_gto_planning.py --robot panda --scene_type shelf --data_dir ../scenereplica_data

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PyBullet Mobile Manipulation Example

cd examples/
python pybullet_gto_planning_mobile.py --robot fetch --scene_type tabletop --data_dir ../scenereplica_data
python pybullet_gto_planning_mobile.py --robot fetch --scene_type shelf --data_dir ../scenereplica_data

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Running with Real Robots

We provide some examples of using the planner with a Fetch robot for the SceneReplica benchmark: https://github.com/IRVLUTD/SceneReplica/tree/gto_planning

  1. Model-based grasping: https://github.com/IRVLUTD/SceneReplica/blob/gto_planning/src/bench_model_based_grasping_gto.py

  2. Model-free grapsing: https://github.com/IRVLUTD/SceneReplica/blob/gto_planning/src/bench_6dof_segmentation_grasping_gto.py

  3. The shelf grasping experiment is conducted with https://github.com/IRVLUTD/SceneReplica/blob/gto_planning/src/model_free_grasping_gto.py

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Note for adding a new robot

  • If joint limits are not presented in the urdf file, urdf_parser_py.urdf will set the joint limits to 0s. Make sure every joint has limits in the urdf file.