ant.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.ant import Ant
from omniisaacgymenvs.tasks.shared.locomotion import LocomotionTask
from pxr import PhysxSchema


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

        self.update_config(sim_config)

        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
        self._num_observations = 60
        self._num_actions = 8
        self._ant_positions = torch.tensor([0, 0, 0.5])
        LocomotionTask.update_config(self)

    def set_up_scene(self, scene) -> None:
        self.get_ant()
        RLTask.set_up_scene(self, scene)
        self._ants = ArticulationView(
            prim_paths_expr="/World/envs/.*/Ant/torso", name="ant_view", reset_xform_properties=False
        )
        scene.add(self._ants)
        return

    def initialize_views(self, scene):
        RLTask.initialize_views(self, scene)
        if scene.object_exists("ant_view"):
            scene.remove_object("ant_view", registry_only=True)
        self._ants = ArticulationView(
            prim_paths_expr="/World/envs/.*/Ant/torso", name="ant_view", reset_xform_properties=False
        )
        scene.add(self._ants)

    def get_ant(self):
        ant = Ant(prim_path=self.default_zero_env_path + "/Ant", name="Ant", translation=self._ant_positions)
        self._sim_config.apply_articulation_settings(
            "Ant", get_prim_at_path(ant.prim_path), self._sim_config.parse_actor_config("Ant")
        )

    def get_robot(self):
        return self._ants

    def post_reset(self):
        self.joint_gears = torch.tensor([15, 15, 15, 15, 15, 15, 15, 15], dtype=torch.float32, device=self._device)
        dof_limits = self._ants.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)
        self.motor_effort_ratio = torch.ones_like(self.joint_gears, device=self._device)

        force_links = ["front_left_foot", "front_right_foot", "left_back_foot", "right_back_foot"]
        self._sensor_indices = torch.tensor(
            [self._ants._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._ants.num_dof)


@torch.jit.script
def get_dof_at_limit_cost(obs_buf, num_dof):
    # type: (Tensor, int) -> Tensor
    return torch.sum(obs_buf[:, 12 : 12 + num_dof] > 0.99, dim=-1)