dlCHOMP
Description
The dlCHOMP
object uses Deep-Learning-Based
Covariant Hamiltonian Optimization for Motion Planning (DLCHOMP) for rigid body
tree robot models. dlCHOMP
optimizes the deep learning network to predict
trajectory that are both smooth and avoid collisions.
For an example showing how to train dlCHOMP
, see Train Deep-Learning-Based CHOMP Optimizer for Motion Planning.
See Pretrained Optimizers to download pretrained
dlCHOMP
objects and their associated training data.
To use CHOMP without deep learning, use the manipulatorCHOMP
object.
The dlCHOMP
object requires the
Deep Learning Toolbox™.
Creation
Description
DLCHOMP = dlCHOMP(
creates a deep-learning CHOMP-based optimizer for a rigid body tree that encodes an
obstacle environment using the specified basis point set (BPS) encoder and guesses a
trajectory with the specified number of waypoints. The robotRBT
,encoder
,numWpts
)robotRBT
,
encoder
, and numWpts
arguments set the
RigidBodyTree
, BPSEncoder
, and
NumWaypoints
properties, respectively.
DLCHOMP = dlCHOMP(___,
specifies properties using one or more name-value arguments in addition to all input
arguments from the previous syntax.Name=Value
)
Properties
Object Functions
generateSamples | Generate datasets for training deep-learning-based CHOMP optimizer |
trainDLCHOMP | Train deep-learning-based CHOMP optimizer |
optimize | Optimize trajectory using deep-learning-based CHOMP |
resetCHOMPOptions | Reset option properties to the last trained state |
show | Visualize deep-learning-based CHOMP trajectory of rigid body tree |
Examples
More About
Tips
Guidance for Training DLCHOMP
Read each condition to determine the appropriate resource for DLCHOMP training or retraining guidance.
If you do not have any trained
dlCHOMP
objects, then see Train Deep-Learning-Based CHOMP Optimizer for Motion Planning. This also applies if you have a traineddlCHOMP
object that does not have the desired BPS encoding, robot model, or environment.If you have a trained
dlCHOMP
object that does not have the desired number of waypoints but does have the desired BPS encoding, robot model, and environment, then see Using Pretrained DLCHOMP Optimizer to Predict Higher Number of Waypoints.If you have a trained
dlCHOMP
object that does not have the desired data options or CHOMP options but does have the desired BPS encoding, robot model, environment, and number of waypoints, then see Using Pretrained DLCHOMP Optimizer in Unseen Obstacle Environment.
References
[1] Tenhumberg, Johannes, Darius Burschka, and Berthold Bauml. “Speeding Up Optimization-Based Motion Planning through Deep Learning.” In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7182–89. Kyoto, Japan: IEEE, 2022. https://doi.org/10.1109/IROS47612.2022.9981717. CloseDeleteEdit
[2] Ratliff, Nathan, Siddhartha Srinivasa, Matt Zucker, and Andrew Bagnell. “CHOMP: Gradient Optimization Techniques for Efficient Motion Planning.” In 2009 IEEE International Conference on Robotics and Automation, 489–94. Kobe, Japan: IEEE, 2009. https://doi.org/10.1109/ROBOT.2009.5152817.
Version History
Introduced in R2024a