humanoid.py

# Copyright (c) 2018-2022, NVIDIA Corporation
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import math

import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.maths import tensor_clamp, torch_rand_float, unscale
from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.humanoid import Humanoid
from omniisaacgymenvs.tasks.shared.locomotion import LocomotionTask
from pxr import PhysxSchema


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

        self.update_config(sim_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 update_config(self, sim_config):
        self._sim_config = sim_config
        self._cfg = sim_config.config
        self._task_cfg = sim_config.task_config
        LocomotionTask.update_config(self)

    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 initialize_views(self, scene):
        RLTask.initialize_views(self, scene)
        if scene.object_exists("humanoid_view"):
            scene.remove_object("humanoid_view", registry_only=True)
        self._humanoids = ArticulationView(
            prim_paths_expr="/World/envs/.*/Humanoid/torso", name="humanoid_view", reset_xform_properties=False
        )
        scene.add(self._humanoids)

    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)

        force_links = ["left_foot", "right_foot"]
        self._sensor_indices = torch.tensor(
            [self._humanoids._body_indices[j] for j in force_links], device=self._device, dtype=torch.long
        )

        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