This repository provides the code to learn torque-limited and collision-free robot trajectories without exceeding limits on the position, velocity, acceleration and jerk of each robot joint.
To use the code, clone the repository with:
git clone https://github.com/translearn/safeMotions.git
The required dependencies can be installed by running:
pip install -r requirements.txt
To generate a random trajectory with a single robot run
python safemotions/random_agent.py --use_gui --check_braking_trajectory_collisions --check_braking_trajectory_torque_limits --torque_limit_factor=0.6 --plot_trajectory
For a demonstration scenario with two robots run
python safemotions/random_agent.py --use_gui --check_braking_trajectory_collisions --robot_scene=1
Collision-free trajectories for three robots can be generated by running
python safemotions/random_agent.py --use_gui --check_braking_trajectory_collisions --robot_scene=2
Various pretrained networks for reaching randomly sampled target points are provided.
Make sure you use ray==1.4.1 to open the pretrained networks.
To generate and plot trajectories for a reaching task with a single industrial robot run
python safemotions/evaluate.py --use_gui --plot_trajectory --plot_actual_torques --checkpoint=industrial/one_robot/collision Trajectories for two and three industrial robots with alternating target points can be generated by running
python safemotions/evaluate.py --use_gui --checkpoint=industrial/two_robots/collision/alternating and
python safemotions/evaluate.py --use_gui --checkpoint=industrial/three_robots/collision/alternating | ARMAR 6 | ARMAR 6x4 | |
|---|---|---|
 |
 |
|
| Alternating target points | --checkpoint=humanoid/armar6/collision/alternating | --checkpoint=humanoid/armar6_x4/collision/alternating |
| Simultaneous target points | --checkpoint=humanoid/armar6/collision/simultaneous | --checkpoint=humanoid/armar6_x4/collision/simultaneous |
| Single target point | --checkpoint=humanoid/armar6/collision/single | --checkpoint=humanoid/armar6_x4/collision/single |
Networks can also be trained from scratch. For instance, a reaching task with a single robot can be learned by running
python safemotions/train.py --logdir=safemotions_training --name=industrial_one_robot_collision --robot_scene=0 --online_trajectory_time_step=0.1 --hidden_layer_activation=swish --online_trajectory_duration=8.0 --obstacle_scene=3 --use_target_points --target_point_sequence=0 --target_point_cartesian_range_scene=0 --target_link_offset="[0, 0, 0.126]" --target_point_radius=0.065 --obs_add_target_point_pos --obs_add_target_point_relative_pos --check_braking_trajectory_collisions --closest_point_safety_distance=0.01 --acc_limit_factor_braking=1.0 --jerk_limit_factor_braking=1.0 --punish_action --action_punishment_min_threshold=0.95 --action_max_punishment=0.4 --target_point_reached_reward_bonus=5 --pos_limit_factor=1.0 --vel_limit_factor=1.0 --acc_limit_factor=1.0 --jerk_limit_factor=1.0 --torque_limit_factor=1.0 --punish_braking_trajectory_min_distance --braking_trajectory_min_distance_max_threshold=0.05 --braking_trajectory_max_punishment=0.5 --last_layer_activation=tanh --solver_iterations=50 --normalize_reward_to_initial_target_point_distance --collision_check_time=0.033 --iterations_per_checkpoint=50 --time=200The corresponding publication is available at https://arxiv.org/abs/2103.03793.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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