humanoid_amp.py

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

from enum import Enum
import numpy as np
import torch
import os

from gym import spaces

from isaacgym import gymapi
from isaacgym import gymtorch

from .amp.humanoid_amp_base import HumanoidAMPBase, dof_to_obs
from .amp.utils_amp import gym_util
from .amp.utils_amp.motion_lib import MotionLib

from isaacgym.torch_utils import *
from isaacgymenvs.utils.torch_jit_utils import *


NUM_AMP_OBS_PER_STEP = 13 + 52 + 28 + 12 # [root_h, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos]


class HumanoidAMP(HumanoidAMPBase):

    class StateInit(Enum):
        Default = 0
        Start = 1
        Random = 2
        Hybrid = 3

    def __init__(self, cfg, sim_device, graphics_device_id, headless):
        self.cfg = cfg

        state_init = cfg["env"]["stateInit"]
        self._state_init = HumanoidAMP.StateInit[state_init]
        self._hybrid_init_prob = cfg["env"]["hybridInitProb"]
        self._num_amp_obs_steps = cfg["env"]["numAMPObsSteps"]
        assert(self._num_amp_obs_steps >= 2)

        self._reset_default_env_ids = []
        self._reset_ref_env_ids = []

        super().__init__(config=self.cfg, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless)

        motion_file = cfg['env'].get('motion_file', "amp_humanoid_backflip.npy")
        motion_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets/amp/motions/" + motion_file)
        self._load_motion(motion_file_path)

        self.num_amp_obs = self._num_amp_obs_steps * NUM_AMP_OBS_PER_STEP

        self._amp_obs_space = spaces.Box(np.ones(self.num_amp_obs) * -np.Inf, np.ones(self.num_amp_obs) * np.Inf)

        self._amp_obs_buf = torch.zeros((self.num_envs, self._num_amp_obs_steps, NUM_AMP_OBS_PER_STEP), device=self.device, dtype=torch.float)
        self._curr_amp_obs_buf = self._amp_obs_buf[:, 0]
        self._hist_amp_obs_buf = self._amp_obs_buf[:, 1:]
        
        self._amp_obs_demo_buf = None

        return

    def post_physics_step(self):
        super().post_physics_step()
        
        self._update_hist_amp_obs()
        self._compute_amp_observations()

        amp_obs_flat = self._amp_obs_buf.view(-1, self.get_num_amp_obs())
        self.extras["amp_obs"] = amp_obs_flat

        return

    def get_num_amp_obs(self):
        return self.num_amp_obs

    @property
    def amp_observation_space(self):
        return self._amp_obs_space

    def fetch_amp_obs_demo(self, num_samples):
        return self.task.fetch_amp_obs_demo(num_samples)

    def fetch_amp_obs_demo(self, num_samples):
        dt = self.dt
        motion_ids = self._motion_lib.sample_motions(num_samples)

        if (self._amp_obs_demo_buf is None):
            self._build_amp_obs_demo_buf(num_samples)
        else:
            assert(self._amp_obs_demo_buf.shape[0] == num_samples)
            
        motion_times0 = self._motion_lib.sample_time(motion_ids)
        motion_ids = np.tile(np.expand_dims(motion_ids, axis=-1), [1, self._num_amp_obs_steps])
        motion_times = np.expand_dims(motion_times0, axis=-1)
        time_steps = -dt * np.arange(0, self._num_amp_obs_steps)
        motion_times = motion_times + time_steps

        motion_ids = motion_ids.flatten()
        motion_times = motion_times.flatten()
        root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
               = self._motion_lib.get_motion_state(motion_ids, motion_times)
        root_states = torch.cat([root_pos, root_rot, root_vel, root_ang_vel], dim=-1)
        amp_obs_demo = build_amp_observations(root_states, dof_pos, dof_vel, key_pos,
                                      self._local_root_obs)
        self._amp_obs_demo_buf[:] = amp_obs_demo.view(self._amp_obs_demo_buf.shape)

        amp_obs_demo_flat = self._amp_obs_demo_buf.view(-1, self.get_num_amp_obs())
        return amp_obs_demo_flat

    def _build_amp_obs_demo_buf(self, num_samples):
        self._amp_obs_demo_buf = torch.zeros((num_samples, self._num_amp_obs_steps, NUM_AMP_OBS_PER_STEP), device=self.device, dtype=torch.float)
        return
        

    def _load_motion(self, motion_file):
        self._motion_lib = MotionLib(motion_file=motion_file, 
                                     num_dofs=self.num_dof,
                                     key_body_ids=self._key_body_ids.cpu().numpy(), 
                                     device=self.device)
        return
    
    def reset_idx(self, env_ids):
        super().reset_idx(env_ids)
        self._init_amp_obs(env_ids)
        return

    def _reset_actors(self, env_ids):
        if (self._state_init == HumanoidAMP.StateInit.Default):
            self._reset_default(env_ids)
        elif (self._state_init == HumanoidAMP.StateInit.Start
              or self._state_init == HumanoidAMP.StateInit.Random):
            self._reset_ref_state_init(env_ids)
        elif (self._state_init == HumanoidAMP.StateInit.Hybrid):
            self._reset_hybrid_state_init(env_ids)
        else:
            assert(False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init))

        self.progress_buf[env_ids] = 0
        self.reset_buf[env_ids] = 0
        self._terminate_buf[env_ids] = 0

        return
    
    def _reset_default(self, env_ids):
        self._dof_pos[env_ids] = self._initial_dof_pos[env_ids]
        self._dof_vel[env_ids] = self._initial_dof_vel[env_ids]

        env_ids_int32 = env_ids.to(dtype=torch.int32)
        self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._initial_root_states),
                                                     gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))

        self.gym.set_dof_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._dof_state),
                                              gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))

        self._reset_default_env_ids = env_ids
        return

    def _reset_ref_state_init(self, env_ids):
        num_envs = env_ids.shape[0]
        motion_ids = self._motion_lib.sample_motions(num_envs)
        
        if (self._state_init == HumanoidAMP.StateInit.Random
            or self._state_init == HumanoidAMP.StateInit.Hybrid):
            motion_times = self._motion_lib.sample_time(motion_ids)
        elif (self._state_init == HumanoidAMP.StateInit.Start):
            motion_times = np.zeros(num_envs)
        else:
            assert(False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init))

        root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
               = self._motion_lib.get_motion_state(motion_ids, motion_times)
        
        self._set_env_state(env_ids=env_ids, 
                            root_pos=root_pos, 
                            root_rot=root_rot, 
                            dof_pos=dof_pos, 
                            root_vel=root_vel, 
                            root_ang_vel=root_ang_vel, 
                            dof_vel=dof_vel)

        self._reset_ref_env_ids = env_ids
        self._reset_ref_motion_ids = motion_ids
        self._reset_ref_motion_times = motion_times
        return

    def _reset_hybrid_state_init(self, env_ids):
        num_envs = env_ids.shape[0]
        ref_probs = to_torch(np.array([self._hybrid_init_prob] * num_envs), device=self.device)
        ref_init_mask = torch.bernoulli(ref_probs) == 1.0

        ref_reset_ids = env_ids[ref_init_mask]
        if (len(ref_reset_ids) > 0):
            self._reset_ref_state_init(ref_reset_ids)

        default_reset_ids = env_ids[torch.logical_not(ref_init_mask)]
        if (len(default_reset_ids) > 0):
            self._reset_default(default_reset_ids)

        return

    def _init_amp_obs(self, env_ids):
        self._compute_amp_observations(env_ids)

        if (len(self._reset_default_env_ids) > 0):
            self._init_amp_obs_default(self._reset_default_env_ids)

        if (len(self._reset_ref_env_ids) > 0):
            self._init_amp_obs_ref(self._reset_ref_env_ids, self._reset_ref_motion_ids,
                                   self._reset_ref_motion_times)
        return

    def _init_amp_obs_default(self, env_ids):
        curr_amp_obs = self._curr_amp_obs_buf[env_ids].unsqueeze(-2)
        self._hist_amp_obs_buf[env_ids] = curr_amp_obs
        return

    def _init_amp_obs_ref(self, env_ids, motion_ids, motion_times):
        dt = self.dt
        motion_ids = np.tile(np.expand_dims(motion_ids, axis=-1), [1, self._num_amp_obs_steps - 1])
        motion_times = np.expand_dims(motion_times, axis=-1)
        time_steps = -dt * (np.arange(0, self._num_amp_obs_steps - 1) + 1)
        motion_times = motion_times + time_steps

        motion_ids = motion_ids.flatten()
        motion_times = motion_times.flatten()
        root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
               = self._motion_lib.get_motion_state(motion_ids, motion_times)
        root_states = torch.cat([root_pos, root_rot, root_vel, root_ang_vel], dim=-1)
        amp_obs_demo = build_amp_observations(root_states, dof_pos, dof_vel, key_pos,
                                      self._local_root_obs)
        self._hist_amp_obs_buf[env_ids] = amp_obs_demo.view(self._hist_amp_obs_buf[env_ids].shape)
        return
    
    def _set_env_state(self, env_ids, root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel):
        self._root_states[env_ids, 0:3] = root_pos
        self._root_states[env_ids, 3:7] = root_rot
        self._root_states[env_ids, 7:10] = root_vel
        self._root_states[env_ids, 10:13] = root_ang_vel
        
        self._dof_pos[env_ids] = dof_pos
        self._dof_vel[env_ids] = dof_vel

        env_ids_int32 = env_ids.to(dtype=torch.int32)
        self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states), 
                                                    gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
        self.gym.set_dof_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._dof_state),
                                                    gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
        return

    def _update_hist_amp_obs(self, env_ids=None):
        if (env_ids is None):
            self._hist_amp_obs_buf[:] = self._amp_obs_buf[:, 0:(self._num_amp_obs_steps - 1)]
        else:
            self._hist_amp_obs_buf[env_ids] = self._amp_obs_buf[env_ids, 0:(self._num_amp_obs_steps - 1)]
        return
    
    def _compute_amp_observations(self, env_ids=None):
        key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :]
        if (env_ids is None):
            self._curr_amp_obs_buf[:] = build_amp_observations(self._root_states, self._dof_pos, self._dof_vel, key_body_pos,
                                                                self._local_root_obs)
        else:
            self._curr_amp_obs_buf[env_ids] = build_amp_observations(self._root_states[env_ids], self._dof_pos[env_ids], 
                                                                    self._dof_vel[env_ids], key_body_pos[env_ids],
                                                                    self._local_root_obs)
        return


#####################################################################
###=========================jit functions=========================###
#####################################################################

@torch.jit.script
def build_amp_observations(root_states, dof_pos, dof_vel, key_body_pos, local_root_obs):
    # type: (Tensor, Tensor, Tensor, Tensor, bool) -> Tensor
    root_pos = root_states[:, 0:3]
    root_rot = root_states[:, 3:7]
    root_vel = root_states[:, 7:10]
    root_ang_vel = root_states[:, 10:13]

    root_h = root_pos[:, 2:3]
    heading_rot = calc_heading_quat_inv(root_rot)

    if (local_root_obs):
        root_rot_obs = quat_mul(heading_rot, root_rot)
    else:
        root_rot_obs = root_rot
    root_rot_obs = quat_to_tan_norm(root_rot_obs)

    local_root_vel = my_quat_rotate(heading_rot, root_vel)
    local_root_ang_vel = my_quat_rotate(heading_rot, root_ang_vel)

    root_pos_expand = root_pos.unsqueeze(-2)
    local_key_body_pos = key_body_pos - root_pos_expand
    
    heading_rot_expand = heading_rot.unsqueeze(-2)
    heading_rot_expand = heading_rot_expand.repeat((1, local_key_body_pos.shape[1], 1))
    flat_end_pos = local_key_body_pos.view(local_key_body_pos.shape[0] * local_key_body_pos.shape[1], local_key_body_pos.shape[2])
    flat_heading_rot = heading_rot_expand.view(heading_rot_expand.shape[0] * heading_rot_expand.shape[1], 
                                               heading_rot_expand.shape[2])
    local_end_pos = my_quat_rotate(flat_heading_rot, flat_end_pos)
    flat_local_key_pos = local_end_pos.view(local_key_body_pos.shape[0], local_key_body_pos.shape[1] * local_key_body_pos.shape[2])
    
    dof_obs = dof_to_obs(dof_pos)

    obs = torch.cat((root_h, root_rot_obs, local_root_vel, local_root_ang_vel, dof_obs, dof_vel, flat_local_key_pos), dim=-1)
    return obs