humanoid.py

# Copyright (c) 2018-2022, NVIDIA Corporation
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from omniisaacgymenvs.tasks.shared.locomotion import LocomotionTask
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.humanoid import Humanoid

from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omni.isaac.core.utils.torch.maths import torch_rand_float, tensor_clamp, unscale

from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path

import numpy as np
import torch
import math

from pxr import PhysxSchema


class HumanoidLocomotionTask(LocomotionTask):
    def __init__(
        self,
        name,
        sim_config,
        env,
        offset=None
    ) -> None:

        self._sim_config = sim_config
        self._cfg = sim_config.config
        self._task_cfg = sim_config.task_config
        self._num_observations = 87
        self._num_actions = 21
        self._humanoid_positions = torch.tensor([0, 0, 1.34])

        LocomotionTask.__init__(self, name=name, env=env)
        return

    def set_up_scene(self, scene) -> None:
        self.get_humanoid()
        RLTask.set_up_scene(self, scene)
        self._humanoids = ArticulationView(prim_paths_expr="/World/envs/.*/Humanoid/torso", name="humanoid_view", reset_xform_properties=False)
        scene.add(self._humanoids)
        return

    def get_humanoid(self):
        humanoid = Humanoid(prim_path=self.default_zero_env_path + "/Humanoid", name="Humanoid", translation=self._humanoid_positions)
        self._sim_config.apply_articulation_settings("Humanoid", get_prim_at_path(humanoid.prim_path), 
            self._sim_config.parse_actor_config("Humanoid"))

    def get_robot(self):
        return self._humanoids

    def post_reset(self):
        self.joint_gears = torch.tensor(
            [
                67.5000, # lower_waist
                67.5000, # lower_waist
                67.5000, # right_upper_arm
                67.5000, # right_upper_arm
                67.5000, # left_upper_arm
                67.5000, # left_upper_arm
                67.5000, # pelvis
                45.0000, # right_lower_arm
                45.0000, # left_lower_arm
                45.0000, # right_thigh: x
                135.0000, # right_thigh: y
                45.0000, # right_thigh: z
                45.0000, # left_thigh: x
                135.0000, # left_thigh: y
                45.0000, # left_thigh: z
                90.0000, # right_knee
                90.0000, # left_knee
                22.5, # right_foot
                22.5, # right_foot
                22.5, # left_foot
                22.5, # left_foot
            ],
            device=self._device,
        )
        self.max_motor_effort = torch.max(self.joint_gears)
        self.motor_effort_ratio = self.joint_gears / self.max_motor_effort
        dof_limits = self._humanoids.get_dof_limits()
        self.dof_limits_lower = dof_limits[0, :, 0].to(self._device)
        self.dof_limits_upper = dof_limits[0, :, 1].to(self._device)

        LocomotionTask.post_reset(self)

    def get_dof_at_limit_cost(self):
        return get_dof_at_limit_cost(self.obs_buf, self.motor_effort_ratio, self.joints_at_limit_cost_scale)


@torch.jit.script
def get_dof_at_limit_cost(obs_buf, motor_effort_ratio, joints_at_limit_cost_scale):
    # type: (Tensor, Tensor, float) -> Tensor
    scaled_cost = joints_at_limit_cost_scale * (torch.abs(obs_buf[:, 12:33]) - 0.98) / 0.02
    dof_at_limit_cost = torch.sum(
        (torch.abs(obs_buf[:, 12:33]) > 0.98) * scaled_cost * motor_effort_ratio.unsqueeze(0), dim=-1
    )
    return dof_at_limit_cost