101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
import numpy as np
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from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
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from legged_gym.utils.helpers import merge_dict
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class A1LeapCfg( A1FieldCfg ):
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#### uncomment this to train non-virtual terrain
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# class sensor( A1FieldCfg.sensor ):
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# class proprioception( A1FieldCfg.sensor.proprioception ):
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# delay_action_obs = True
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# latency_range = [0.04-0.0025, 0.04+0.0075]
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#### uncomment the above to train non-virtual terrain
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class terrain( A1FieldCfg.terrain ):
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max_init_terrain_level = 2
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border_size = 5
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slope_treshold = 20.
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curriculum = True
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BarrierTrack_kwargs = merge_dict(A1FieldCfg.terrain.BarrierTrack_kwargs, dict(
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options= [
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"leap",
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],
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leap= dict(
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length= (0.2, 1.0),
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depth= (0.4, 0.8),
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height= 0.2,
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),
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virtual_terrain= False, # Change this to False for real terrain
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no_perlin_threshold= 0.06,
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))
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TerrainPerlin_kwargs = merge_dict(A1FieldCfg.terrain.TerrainPerlin_kwargs, dict(
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zScale= [0.05, 0.1],
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))
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class commands( A1FieldCfg.commands ):
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class ranges( A1FieldCfg.commands.ranges ):
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lin_vel_x = [1.0, 1.5]
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lin_vel_y = [0.0, 0.0]
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ang_vel_yaw = [0., 0.]
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class termination( A1FieldCfg.termination ):
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# additional factors that determines whether to terminates the episode
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termination_terms = [
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"roll",
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"pitch",
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"z_low",
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"z_high",
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"out_of_track",
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]
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roll_kwargs = merge_dict(A1FieldCfg.termination.roll_kwargs, dict(
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threshold= 0.4,
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leap_threshold= 0.4,
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))
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z_high_kwargs = merge_dict(A1FieldCfg.termination.z_high_kwargs, dict(
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threshold= 2.0,
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))
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class rewards( A1FieldCfg.rewards ):
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class scales:
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tracking_ang_vel = 0.05
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world_vel_l2norm = -1.
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legs_energy_substeps = -1e-6
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alive = 2.
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penetrate_depth = -4e-3
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penetrate_volume = -4e-3
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exceed_dof_pos_limits = -1e-1
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exceed_torque_limits_i = -2e-1
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class curriculum( A1FieldCfg.curriculum ):
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penetrate_volume_threshold_harder = 9000
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penetrate_volume_threshold_easier = 10000
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penetrate_depth_threshold_harder = 300
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penetrate_depth_threshold_easier = 5000
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class A1LeapCfgPPO( A1FieldCfgPPO ):
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class algorithm( A1FieldCfgPPO.algorithm ):
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entropy_coef = 0.0
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clip_min_std = 0.2
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class runner( A1FieldCfgPPO.runner ):
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policy_class_name = "ActorCriticRecurrent"
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experiment_name = "field_a1"
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run_name = "".join(["Skill",
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("Multi" if len(A1LeapCfg.terrain.BarrierTrack_kwargs["options"]) > 1 else (A1LeapCfg.terrain.BarrierTrack_kwargs["options"][0] if A1LeapCfg.terrain.BarrierTrack_kwargs["options"] else "PlaneWalking")),
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("_propDelay{:.2f}-{:.2f}".format(
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A1LeapCfg.sensor.proprioception.latency_range[0],
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A1LeapCfg.sensor.proprioception.latency_range[1],
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) if A1LeapCfg.sensor.proprioception.delay_action_obs else ""
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),
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("_pEnergySubsteps{:.0e}".format(A1LeapCfg.rewards.scales.legs_energy_substeps) if A1LeapCfg.rewards.scales.legs_energy_substeps != -2e-6 else ""),
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("_virtual" if A1LeapCfg.terrain.BarrierTrack_kwargs["virtual_terrain"] else ""),
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])
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resume = True
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load_run = "{Your traind walking model directory}"
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load_run = "{Your virtually trained leap model directory}"
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max_iterations = 20000
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save_interval = 500
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