ark_ml

Machine learning backbone for the Ark Robotics Framework, providing core models, algorithms, and tools to enable intelligent perception, decision-making, and control in robotic applications.

Installation

1. Set up environment

1. Create and activate conda/virtual environment.

conda create -n ark_env python=3.11 -y
conda activate ark_env

2. Clone repository.

  - (ssh) `git clone [email protected]:Robotics-Ark/ark_ml.git`
  - (https) `git clone https://github.com/Robotics-Ark/ark_ml.git`

3. Install ark_ml framework.

pip install -e ark_ml

4. Install other Ark modules. The ML backbone depends on additional Ark modules. Make sure the following are also installed:

• ark_framework
• ark_types

pip install -e ark_framework
pip install -e ark_types

Rollout

Use the trained model for rollouts:

HYDRA_FULL_ERROR=1 arkml-policy algo=<ml_algorithm>  \
  algo.model.model_path=path/to/the/model \

Policy services available

Start Service

Start the policy service to get machine learning based action prediction

PolicyName/policy/stop

PolicyName can be set through command line by using below command

HYDRA_FULL_ERROR=1 arkml-policy algo=<ml_algorithm>  \
  algo.model.model_path=path/to/the/model \
  policy_node_name=policy_name

Stop Service

Stop the policy service to pause machine learning based action prediction

PolicyName/policy/stop

Reset Service

Reset the policy state

PolicyName/policy/stop

Predict Service

predict next action

PolicyName/policy/predict

Client Service

A client service can be started using below command

python  ark_ml/arkml/examples/franka_pick_place/franka_pick_place.py --max_step 1000 --n_episodes 5 --step_sleep 0.1

Training

Train a model with a dataset:

CUDA_VISIBLE_DEVICES=0 HYDRA_FULL_ERROR=1 \
arkml.tools.train algo=<ml_algorithm> \
 data.dataset_path=/path/to/dataset \
 output_dir=/output/path