init

jmoves/Dockerfile

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+FROM ubuntu:22.04
+
+RUN apt-get update 
+
+# Install base utilities
+RUN apt-get update && \
+    apt-get install -y wget git && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install miniconda
+ENV CONDA_DIR /opt/conda
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+     /bin/bash ~/miniconda.sh -b -p /opt/conda
+
+
+# Put conda in path so we can use conda activate
+RUN useradd -m jovyan
+
+ENV PATH=$CONDA_DIR/bin:$PATH
+COPY environment_jmoves.yml .
+
+# RUN conda env create -f environment_jmoves.yml
+RUN conda env update -n base --file environment_jmoves.yml
+RUN conda init
+
+SHELL ["conda", "run", "-n", "base", "/bin/bash", "-c"]
+
+# Install jupiter stuff
+RUN conda run -n base pip install jupyter notebook voila
+RUN conda run -n base jupyter server extension enable voila --sys-prefix --enable_nbextensions=True
+
+# Install our package 
+WORKDIR /home/jovyan
+
+COPY . ./jmoves_env
+RUN conda run -n base pip install -e ./jmoves_env
+RUN conda run -n base pip install ./jmoves_env/meshcat-0.3.2.tar.gz
+
+
+USER jovyan
+ENV PATH=$CONDA_DIR/bin:$PATH
+RUN conda init
+
+CMD ["/bin/bash", "-c", "if [ -d /usr/local/bin/before-notebook.d ]; then for file in /usr/local/bin/before-notebook.d/*; do $file ; done; fi && jupyter notebook --no-browser --NotebookApp.allow_origin='*' --NotebookApp.token='' --ip=0.0.0.0 --NotebookApp.allow_remote_access=True"]

jmoves/apps/article/create_reward_manager.py

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+import multiprocessing
+from networkx import Graph
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pymoo.algorithms.soo.nonconvex.pso import PSO
+from pymoo.core.problem import StarmapParallelization
+from auto_robot_design.generator.restricted_generator.two_link_generator import TwoLinkGenerator, visualize_constrains
+
+from auto_robot_design.optimization.saver import (
+    ProblemSaver, )
+from auto_robot_design.description.builder import jps_graph2pinocchio_robot
+from auto_robot_design.description.utils import draw_joint_point
+from auto_robot_design.optimization.problems import CalculateCriteriaProblemByWeigths, get_optimizing_joints
+from auto_robot_design.optimization.optimizer import PymooOptimizer
+from auto_robot_design.pinokla.calc_criterion import ActuatedMass, EffectiveInertiaCompute, ImfCompute, ManipCompute, MovmentSurface, NeutralPoseMass, TranslationErrorMSE, ManipJacobian
+from auto_robot_design.pinokla.criterion_agregator import CriteriaAggregator
+from auto_robot_design.pinokla.criterion_math import ImfProjections
+from auto_robot_design.pinokla.default_traj import convert_x_y_to_6d_traj_xz, get_horizontal_trajectory, get_simple_spline, get_vertical_trajectory, create_simple_step_trajectory, get_workspace_trajectory
+from auto_robot_design.optimization.rewards.reward_base import PositioningReward, PositioningConstrain, PositioningErrorCalculator, RewardManager
+from auto_robot_design.optimization.rewards.jacobian_and_inertia_rewards import HeavyLiftingReward, AccelerationCapability, MeanHeavyLiftingReward, MinAccelerationCapability
+from auto_robot_design.optimization.rewards.pure_jacobian_rewards import EndPointZRRReward, VelocityReward, ForceEllipsoidReward
+from auto_robot_design.optimization.rewards.inertia_rewards import MassReward
+from auto_robot_design.description.builder import ParametrizedBuilder, DetailedURDFCreatorFixedEE, jps_graph2pinocchio_robot, MIT_CHEETAH_PARAMS_DICT
+
+
+def get_manager_preset_2_stair_climber(graph: Graph, optimizing_joints: dict, workspace_traj: np.ndarray, step_trajs: list[np.ndarray], squat_trajs: list[np.ndarray]):
+    dict_trajectory_criteria = {
+        "MASS": NeutralPoseMass()
+    }
+    # criteria calculated for each point on the trajectory
+    dict_point_criteria = {
+        "Effective_Inertia": EffectiveInertiaCompute(),
+        "Actuated_Mass": ActuatedMass(),
+        "Manip_Jacobian": ManipJacobian(MovmentSurface.XZ)
+    }
+
+    crag = CriteriaAggregator(dict_point_criteria, dict_trajectory_criteria)
+    error_calculator = PositioningErrorCalculator(
+        error_key='error', jacobian_key="Manip_Jacobian")
+    soft_constrain = PositioningConstrain(
+        error_calculator=error_calculator, points=[workspace_traj])
+    reward_manager = RewardManager(crag=crag)
+    reward_manager.add_trajectory_aggregator
+    acceleration_capability = MinAccelerationCapability(manipulability_key='Manip_Jacobian',
+                                                        trajectory_key="traj_6d", error_key="error", actuated_mass_key="Actuated_Mass")
+
+    heavy_lifting = HeavyLiftingReward(
+        manipulability_key='Manip_Jacobian', trajectory_key="traj_6d", error_key="error", mass_key="MASS")
+    # reward_manager.agg_list =
+    reward_manager.add_trajectory(step_trajs[0], 0)
+    reward_manager.add_trajectory(step_trajs[1], 1)
+    reward_manager.add_trajectory(step_trajs[2], 2)
+
+    reward_manager.add_trajectory(squat_trajs[0], 10)
+    reward_manager.add_trajectory(squat_trajs[1], 11)
+    reward_manager.add_trajectory(squat_trajs[2], 12)
+
+    reward_manager.add_reward(acceleration_capability, 0, weight=1)
+    reward_manager.add_reward(acceleration_capability, 1, weight=1)
+    reward_manager.add_reward(acceleration_capability, 2, weight=1)
+
+    reward_manager.add_reward(heavy_lifting, 10, weight=1)
+    reward_manager.add_reward(heavy_lifting, 11, weight=1)
+    reward_manager.add_reward(heavy_lifting, 12, weight=1)
+
+    reward_manager.add_trajectory_aggregator([0, 1, 2], 'mean')
+    reward_manager.add_trajectory_aggregator([10, 11, 12], 'mean')
+
+    reward_manager.close_trajectories()
+
+    return reward_manager, crag, soft_constrain
+
+
+
+def get_manager_preset_2_stair_single(graph: Graph, optimizing_joints: dict, workspace_traj: np.ndarray, step_trajs: list[np.ndarray], squat_trajs: list[np.ndarray]):
+    dict_trajectory_criteria = {
+        "MASS": NeutralPoseMass()
+    }
+    # criteria calculated for each point on the trajectory
+    dict_point_criteria = {
+        "Effective_Inertia": EffectiveInertiaCompute(),
+        "Actuated_Mass": ActuatedMass(),
+        "Manip_Jacobian": ManipJacobian(MovmentSurface.XZ)
+    }
+
+    crag = CriteriaAggregator(dict_point_criteria, dict_trajectory_criteria)
+    error_calculator = PositioningErrorCalculator(
+        error_key='error', jacobian_key="Manip_Jacobian")
+    soft_constrain = PositioningConstrain(
+        error_calculator=error_calculator, points=[workspace_traj])
+    reward_manager = RewardManager(crag=crag)
+    reward_manager.add_trajectory_aggregator
+    acceleration_capability = MinAccelerationCapability(manipulability_key='Manip_Jacobian',
+                                                        trajectory_key="traj_6d", error_key="error", actuated_mass_key="Actuated_Mass")
+
+    heavy_lifting = HeavyLiftingReward(
+        manipulability_key='Manip_Jacobian', trajectory_key="traj_6d", error_key="error", mass_key="MASS")
+    # reward_manager.agg_list =
+    reward_manager.add_trajectory(step_trajs[0], 0)
+    reward_manager.add_trajectory(step_trajs[1], 1)
+    reward_manager.add_trajectory(step_trajs[2], 2)
+
+    reward_manager.add_trajectory(squat_trajs[0], 10)
+    reward_manager.add_trajectory(squat_trajs[1], 11)
+    reward_manager.add_trajectory(squat_trajs[2], 12)
+
+    reward_manager.add_reward(acceleration_capability, 0, weight=1)
+    reward_manager.add_reward(acceleration_capability, 1, weight=1)
+    reward_manager.add_reward(acceleration_capability, 2, weight=1)
+
+    reward_manager.add_reward(heavy_lifting, 10, weight=1)
+    reward_manager.add_reward(heavy_lifting, 11, weight=1)
+    reward_manager.add_reward(heavy_lifting, 12, weight=1)
+
+    reward_manager.add_trajectory_aggregator([0, 1, 2], 'mean')
+    reward_manager.add_trajectory_aggregator([10, 11, 12], 'mean')
+
+    reward_manager.close_trajectories()
+
+    return reward_manager, crag, soft_constrain

jmoves/apps/article/crerate_squat_trq_plots.py

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+from auto_robot_design.pinokla.criterion_agregator import load_criterion_traj
+import multiprocessing
+from networkx import Graph
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pymoo.algorithms.soo.nonconvex.pso import PSO
+from pymoo.core.problem import StarmapParallelization
+from auto_robot_design.generator.restricted_generator.two_link_generator import TwoLinkGenerator, visualize_constrains
+
+from auto_robot_design.optimization.saver import (
+    ProblemSaver, )
+from auto_robot_design.description.builder import jps_graph2pinocchio_robot
+from auto_robot_design.description.utils import draw_joint_point
+from auto_robot_design.optimization.problems import CalculateCriteriaProblemByWeigths, CalculateMultiCriteriaProblem, get_optimizing_joints
+from auto_robot_design.optimization.optimizer import PymooOptimizer
+from auto_robot_design.pinokla.analyze_squat_history import get_sample_torque_traj_from_sample_multi
+from auto_robot_design.pinokla.calc_criterion import ActuatedMass, EffectiveInertiaCompute, ImfCompute, ManipCompute, MovmentSurface, NeutralPoseMass, TranslationErrorMSE, ManipJacobian
+from auto_robot_design.pinokla.criterion_agregator import CriteriaAggregator
+from auto_robot_design.pinokla.criterion_math import ImfProjections
+from auto_robot_design.pinokla.default_traj import convert_x_y_to_6d_traj_xz, get_horizontal_trajectory, get_simple_spline, get_vertical_trajectory, create_simple_step_trajectory, get_workspace_trajectory
+from auto_robot_design.optimization.rewards.reward_base import PositioningReward, PositioningConstrain, PositioningErrorCalculator, RewardManager
+from auto_robot_design.optimization.rewards.jacobian_and_inertia_rewards import HeavyLiftingReward, AccelerationCapability, MeanHeavyLiftingReward, MinAccelerationCapability
+from auto_robot_design.optimization.rewards.pure_jacobian_rewards import EndPointZRRReward, VelocityReward, ForceEllipsoidReward
+from auto_robot_design.optimization.rewards.inertia_rewards import MassReward
+from auto_robot_design.description.builder import ParametrizedBuilder, DetailedURDFCreatorFixedEE, jps_graph2pinocchio_robot, MIT_CHEETAH_PARAMS_DICT
+from auto_robot_design.description.builder import DetailedURDFCreatorFixedEE, ParametrizedBuilder, jps_graph2urdf_by_bulder
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from auto_robot_design.description.actuators import t_motor_actuators
+
+from auto_robot_design.description.utils import (
+    all_combinations_active_joints_n_actuator, )
+
+from auto_robot_design.generator.restricted_generator.two_link_generator import TwoLinkGenerator
+from auto_robot_design.pinokla.squat import SquatHopParameters, SimulateSquatHop
+from auto_robot_design.optimization.analyze import get_optimizer_and_problem, get_pareto_sample_linspace, get_pareto_sample_histogram, get_urdf_from_problem
+from pathlib import Path
+
+
+def get_all_files_in_dir(directory):
+    try:
+        # Create a Path object for the directory
+        path = Path(directory)
+
+        # Use the glob method to match all files in the directory
+        files = [str(file) for file in path.glob('*') if file.is_file()]
+
+        return files
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return []
+
+
+def get_metrics(loaded_dict: dict):
+    trq_1 = loaded_dict["tau"][:, 0]
+    trq_2 = loaded_dict["tau"][:, 1]
+    max_1 = np.max(np.abs(trq_1))
+    max_2 = np.max(np.abs(trq_2))
+
+    agr_max = max([max_1, max_2])
+
+    mean_1 = np.mean(np.abs(trq_1))
+    mean_2 = np.mean(np.abs(trq_2))
+
+    agr_mean = mean_1 + mean_2
+
+    max_diff_1 = np.max(np.abs(np.diff(trq_1)))
+    max_diff_2 = np.max(np.abs(np.diff(trq_2)))
+
+    agr_diff = max([max_diff_1, max_diff_2])
+    return agr_mean, agr_max, agr_diff, loaded_dict["Reward"], loaded_dict["X"]
+
+
+def get_all_vector_metrics(directory):
+    sim_res_files = get_all_files_in_dir(directory)
+    sim_res = list(map(load_criterion_traj, sim_res_files))
+    agr_mean_list = []
+    agr_max_list = []
+    agr_diff_list = []
+    reword_list = []
+    param_x_list = []
+    for sim_res_i in sim_res:
+        agr_mean, agr_max, agr_diff, reword, param_x = get_metrics(sim_res_i)
+        agr_mean_list.append(agr_mean)
+        agr_max_list.append(agr_max)
+        agr_diff_list.append(agr_diff)
+        reword_list.append(reword)
+        param_x_list.append(param_x)
+    return agr_mean_list, agr_max_list, agr_diff_list, reword_list, param_x_list
+
+
+PATH_CS = "results\\multi_opti_preset2\\topology_8_2024-05-30_10-40-12\\squat_compare"
+agr_mean_list, agr_max_list, agr_diff_list, reword_list, param_x_list = get_all_vector_metrics(
+    PATH_CS)
+save_p = Path(PATH_CS + "/" + "plots" + "/")
+save_p.mkdir(parents=True, exist_ok=True)
+
+plt.figure()
+plt.scatter(np.array(reword_list)[:, 0], np.array(
+    reword_list)[:, 1], c=agr_mean_list, cmap="rainbow")
+plt.colorbar()
+plt.title("Mean torque in squat_sim on Pareto front")
+plt.xlabel("ACC Capability")
+plt.ylabel("HeavyLifting")
+
+save_current1 = save_p / "Mean_torque_in_squat_sim_on_Pareto_front.svg"
+save_current2 = save_p / "Mean_torque_in_squat_sim_on_Pareto_front.png"
+plt.savefig(save_current1)
+plt.savefig(save_current2)
+
+plt.figure()
+plt.scatter(np.array(reword_list)[:, 0], np.array(
+    reword_list)[:, 1], c=agr_max_list, cmap="rainbow")
+plt.colorbar()
+plt.title("Max torque in squat_sim on Pareto front")
+plt.xlabel("ACC Capability")
+plt.ylabel("HeavyLifting")
+
+save_current1 = save_p / "Max_torque_in_squat_sim_on_Pareto_front.svg"
+save_current2 = save_p / "Max_torque_in_squat_sim_on_Pareto_front.png"
+plt.savefig(save_current1)
+plt.savefig(save_current2)
+
+
+plt.figure()
+plt.scatter(np.array(reword_list)[:, 0], np.array(
+    reword_list)[:, 1], c=agr_diff_list, cmap="rainbow")
+plt.colorbar()
+plt.title("Max torque diff in squat_sim on Pareto front")
+plt.xlabel("ACC Capability")
+plt.ylabel("HeavyLifting")
+plt.savefig("Max torque diff in squat_sim on Pareto front")
+save_current1 = save_p / "Max_torque_diff_in_squat_sim_on_Pareto_front.svg"
+save_current2 = save_p / "Max_torque_diff_in_squat_sim_on_Pareto_front.png"
+plt.savefig(save_current1)
+plt.savefig(save_current2)
+plt.show()

jmoves/apps/article/history_mupti_open.py

@@ -0,0 +1,24 @@
+from pathlib import Path
+import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.pyplot as plt
+from auto_robot_design.optimization.analyze import get_optimizer_and_problem, get_pareto_sample_linspace, get_pareto_sample_histogram
+
+optimizer, problem, res = get_optimizer_and_problem(
+    "results\\multi_opti_preset2\\topology_0_2024-05-29_18-48-58")
+sample_x, sample_F = get_pareto_sample_linspace(res, 10)
+sample_x2, sample_F2 = get_pareto_sample_histogram(res, 10)
+
+
+
+save_p = Path(str(PATH_CS) + "/" + "plots")
+save_p.mkdir(parents=True, exist_ok=True)
+
+history_mean = np.array(optimizer.history["Mean"])
+
+plt.figure()
+
+plt.figure()
+plt.scatter(sample_F2[:, 0], sample_F2[:, 1])
+plt.title("from res2")
+plt.show()