Metadata-Version: 2.1
Name: safemotions
Version: 0.2.4
Summary: Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors.
Home-page: https://github.com/translearn/safemotions
Author: Jonas C. Kiemel
Author-email: jonas.kiemel@kit.edu
License: UNKNOWN
Description: # Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors
        This python package 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.
        
        ## Installation
        
        The package can be installed by running
        
            pip install safemotions
        
        ## Trajectory generation
        
        To generate a random trajectory with a single robot run
        
            python -m safemotions.random_agent
        
        For a demonstration scenario with two robots run
        
            python -m safemotions.random_agent --robot_scene=1
        
        Collision-free trajectories for three robots can be generated by running
        
            python -m safemotions.random_agent --robot_scene=2
        
        
        ## Pretrained networks
        
        Pretrained networks for various reaching tasks are provided. \
        To generate and plot trajectories for a reaching task with a single robot run
        
        ```bash
        python -m safemotions.evaluate --checkpoint=one_robot/P_CT_S_5_J_A --use_gui --plot_trajectory --plot_actual_torques
        ```
        Trajectories for two and three robots with alternating target points can be generated by running
        
        ```bash
        python -m safemotions.evaluate --checkpoint=two_robots/P_C_S_1_J_A_D_5_T_A --use_gui 
        ```
        and
        ```bash
        python -m safemotions.evaluate --checkpoint=three_robots/P_C_S_1_J_A_D_5_T_A --use_gui 
        ```
        
        ## Training
        
        Networks can also be trained from scratch. For instance, a reaching task with a single robot can be learned by running 
        ```bash
        python -m safemotions.train --logdir=safemotions_training --name=One_robot_P_CT_S_5_J_A --robot_scene=0 --online_trajectory_time_step=0.1 --online_trajectory_duration=8.0 --use_target_points --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 --obstacle_scene=3 --obstacle_use_computed_actual_values --use_braking_trajectory_method --closest_point_safety_distance=0.05 --check_braking_trajectory_torque_limits --acc_limit_factor_braking=0.75 --jerk_limit_factor_braking=0.75 --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 --iterations_per_checkpoint=100 --time=216
        ```
        
        ## Publication
        The corresponding publication is available at [https://arxiv.org/abs/2103.03793](https://arxiv.org/abs/2103.03793).
        
        [![Video](https://img.youtube.com/vi/5YpUhMx1xZM/0.jpg)](https://www.youtube.com/watch?v=5YpUhMx1xZM)
        
        ## Disclaimer
        
        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.
Platform: UNKNOWN
Requires-Python: >=3.5, <3.9
Description-Content-Type: text/markdown
