ant.py

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

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

from pxr import PhysxSchema

import numpy as np
import torch
import math


class AntLocomotionTask(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 = 60
        self._num_actions = 8
        self._ant_positions = torch.tensor([0, 0, 0.5])

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

    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 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)

        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)