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python
a year ago
62 kB
3
Indexable
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
import math
from isaacgymenvs.utils.torch_jit_utils import (
to_torch,
get_axis_params,
torch_rand_float,
quat_rotate_inverse,
)
from isaacgymenvs.tasks.base.vec_task import VecTask
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, quat_rotate, quat_rotate_inverse
from typing import Tuple, Dict
# PERMUTATION_MATRIX = torch.tensor([
# [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
# [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
# [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
# [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
# [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
# [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]]
# , dtype=torch.float32)
# KP = torch.tensor(2 * [2.0, 2.0, 34.0, 34.0, 0.75, 0.75]) * torch.square(
# torch.tensor(2 * [100, 122, 24.42, 24.42, 102, 125])
# ) # from PACT_dimensioning_beta.pdf
KP = torch.tensor([15000.0, 15000.0, 15000.0, 15000.0, 5000.0, 5000.0] * 2)
# KD = torch.tensor(2 * [0.000638, 0.0029, 0.000638, 0.001, 0.0007, 0.0007])
KD = torch.tensor([0.01, 0.01, 0.01, 0.01, 0.01, 0.01] * 2)
ACTIONS_MIRROR_MAT = torch.tensor([
[ 0., 0., 0., 0., 0., 0., -1., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., -1., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., -1.],
[-1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., -1., 0., 0., 0., 0., 0., 0.]
], dtype=torch.float32)
OBSERVATION_MIRROR_MAT = torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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JOINTS = {
"LeftFrontalHipJoint": {"lower": -0.244346, "upper": 0.279253},
"LeftTransverseHipJoint": {"lower": -0.174533, "upper": 0.523599},
"LeftSagittalHipJoint": {"lower": -2.268928, "upper": 0.261799},
"LeftSagittalKneeJoint": {"lower": 0.0, "upper": 2.268928},
"LeftSagittalAnkleJoint": {"lower": -0.436332, "upper": 0.261799},
"LeftFrontalAnkleJoint": {"lower": -0.279253, "upper": 0.331613},
"RightFrontalHipJoint": {"lower": -0.279253, "upper": 0.244346},
"RightTransverseHipJoint": {"lower": -0.523599, "upper": 0.174533},
"RightSagittalHipJoint": {"lower": -2.268928, "upper": 0.261799},
"RightSagittalKneeJoint": {"lower": 0.0, "upper": 2.268928},
"RightSagittalAnkleJoint": {"lower": -0.436332, "upper": 0.261799},
"RightFrontalAnkleJoint": {"lower": -0.331613, "upper": 0.279253},
}
class EveBeta(VecTask):
def __init__(
self,
cfg,
rl_device,
sim_device,
graphics_device_id,
headless,
virtual_screen_capture,
force_render,
):
self.cfg = cfg
self.low_limit =torch.tensor([-0.244346, -0.174533, -2.268928, 0. , -0.436332, -0.279253,
-0.279253, -0.523599, -2.268928, 0. , -0.436332, -0.331613] ) .to(rl_device)
self.high_limit = torch.tensor([0.279253, 0.523599, 0.261799, 2.268928, 0.261799, 0.331613,
0.244346, 0.174533, 0.261799, 2.268928, 0.261799, 0.279253]).to(rl_device)
self._action_orig_mean = (self.high_limit + self.low_limit) / 2.0
self._action_orig_dev = (self.high_limit - self.low_limit) / 2.0
self.log_freq = self.cfg["env"]["learn"]["logFrequency"]
self.robot_weight = 1151.34696144
self.foot_width, self.foot_length = 0.131, 0.2811
self.contact_wrenches_norm = torch.tile( self.robot_weight * torch.tensor([1.0, self.foot_width, self.foot_length]), (2,)).to(rl_device)
# normalization
self.lin_vel_scale = self.cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self.cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self.cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self.cfg["env"]["learn"]["dofVelocityScale"]
self.action_scale = self.cfg["env"]["control"]["actionScale"]
self.reward_config = self.cfg["env"]["learn"]["cassie_reward"]
self.original_push_freq = self.cfg["env"]["learn"]["pushFrequency"]
self.push_freq = self.cfg["env"]["learn"]["pushFrequency"]
for key in self.reward_config:
for mini_key in self.reward_config[key]:
self.reward_config[key][mini_key] = torch.tensor(
self.reward_config[key][mini_key], device=rl_device
)
self.push_time = self.cfg["sim"]["pushTime"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self.cfg["env"]["learn"][
"linearVelocityXYRewardScale"
]
self.rew_scales["ang_vel_z"] = self.cfg["env"]["learn"][
"angularVelocityZRewardScale"
]
self.rew_scales["torque"] = self.cfg["env"]["learn"]["torqueRewardScale"]
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
# command ranges
self.command_x_range = self.cfg["env"]["randomCommandVelocityRanges"][
"linear_x"
]
self.command_y_range = self.cfg["env"]["randomCommandVelocityRanges"][
"linear_y"
]
self.command_yaw_range = self.cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# plane params
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
# base init state
pos = self.cfg["env"]["baseInitState"]["pos"]
rot = self.cfg["env"]["baseInitState"]["rot"]
v_lin = self.cfg["env"]["baseInitState"]["vLinear"]
v_ang = self.cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
mean_joint_angles = [
(joint["lower"] + joint["upper"]) /5.0 for joint in JOINTS.values()
]
# default joint positions
# self.named_default_joint_angles = self.cfg["env"]["defaultJointAngles"]
self.named_default_joint_angles = dict(zip(JOINTS.keys(), mean_joint_angles))
self.cfg["env"]["numObservations"] = 65
self.cfg["env"]["numActions"] = 12
self.use_new_reward = self.cfg["env"]["learn"]["useNewReward"]
self.stepper_state = 0
super().__init__(
config=self.cfg,
rl_device=rl_device,
sim_device=sim_device,
graphics_device_id=graphics_device_id,
headless=headless,
virtual_screen_capture=virtual_screen_capture,
force_render=force_render,
)
# other
self.dt = self.sim_params.dt
self.max_episode_length_s = self.cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
if self.viewer != None:
p = self.cfg["env"]["viewer"]["pos"]
lookat = self.cfg["env"]["viewer"]["lookat"]
cam_pos = gymapi.Vec3(p[0], p[1], p[2])
cam_target = gymapi.Vec3(lookat[0], lookat[1], lookat[2])
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
rigid_body_state = self.gym.acquire_rigid_body_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
torques = self.gym.acquire_dof_force_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
sensors_per_env = 2
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.change_freq = False
# create some wrapper tensors for different slices
self.clocks = torch.rand(
self.num_envs, dtype=torch.float, device=self.device, requires_grad=False
)
# round to the closest multiple of dt
self.clocks -= self.clocks % self.dt
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_state).view(
self.num_envs, -1, 13
)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(
self.num_envs, -1, 3
) # shape: num_envs, num_bodies, xyz axis
self.torques = gymtorch.wrap_tensor(torques).view(self.num_envs, self.num_dof)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(
self.num_envs, sensors_per_env * 6
)
self.commands = torch.zeros(
self.num_envs, 3, dtype=torch.float, device=self.device, requires_grad=False
)
self.commands_y = self.commands.view(self.num_envs, 3)[..., 1]
self.commands_x = self.commands.view(self.num_envs, 3)[..., 0]
self.commands_yaw = self.commands.view(self.num_envs, 3)[..., 2]
self.default_dof_pos = torch.zeros_like(
self.dof_pos, dtype=torch.float, device=self.device, requires_grad=False
)
self.cmd_buffer = torch.zeros(
self.num_envs, 12, dtype=torch.float, device=self.device, requires_grad=False
)
self.used_quantities = {
"odometry_x": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"odometry_y": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"odometry_roll": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"odometry_pitch": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"odometry_yaw": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"left_contact_cost": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"right_contact_cost": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"velocity_left": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"velocity_right": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"torque": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
"friction": (
torch.tensor(0.0, device=self.device),
torch.tensor(1.0, device=self.device),
),
}
self.first_call = True
for i in range(self.cfg["env"]["numActions"]):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
# initialize some data used later on
self.extras = {}
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:] = to_torch(self.base_init_state, device=self.device, requires_grad=False)
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self._initialize_observation_space()
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def _initialize_observation_space(self) -> None:
# Extract some proxies
nflex = 12
nmotors = 12
self.observation_space_bounds =[
torch.tensor([[-1.0], [1.0]]), # Cosine
torch.tensor([[-1.0], [1.0]]), # Sine
torch.tensor([[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]), # Base orientation
torch.tensor([[-np.inf, -np.inf, -np.inf], [np.inf, np.inf, np.inf]]), # Base angular velocity
torch.tensor([[0.0,-1.0,-1.0], [2.0,1.0,1.0]]), # Contact wrenches
torch.tensor([[0.0,-1.0,-1.0], [2.0,1.0,1.0]]), # Contact wrenches
torch.tensor([self.low_limit.tolist(), self.high_limit.tolist()]), # Joint positions
torch.tensor([[-torch.inf] * nmotors, [torch.inf] * nmotors]), # Joint velocities
torch.tensor([[-np.pi]* nflex , [np.pi]* nflex]), # flexes
torch.tensor([self.low_limit.tolist(), self.high_limit.tolist()]), # cmd_positions
torch.tensor([[-np.inf,-np.inf, -np.pi], [np.inf,np.inf, np.pi]]), # cmd_xyy
]
for i in range(len(self.observation_space_bounds)):
self.observation_space_bounds[i] = self.observation_space_bounds[i].to(self.device)
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(
self.device_id,
self.graphics_device_id,
self.physics_engine,
self.sim_params,
)
self._create_ground_plane()
self._create_envs(
self.num_envs, self.cfg["env"]["envSpacing"], int(np.sqrt(self.num_envs))
)
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
asset_root = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "../../assets"
)
asset_file = "urdf/eve/eve_beta/exo_with_patient.urdf"
asset_options = gymapi.AssetOptions()
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
asset_options.collapse_fixed_joints = True
asset_options.replace_cylinder_with_capsule = True
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = self.cfg["env"]["urdfAsset"]["fixBaseLink"]
#asset_options.density = 0.001
# asset_options.thickness = 0.01
asset_options.disable_gravity = False
beta_asset = self.gym.load_asset(
self.sim, asset_root, asset_file, asset_options
)
self.num_dof = self.gym.get_asset_dof_count(beta_asset)
self.num_bodies = self.gym.get_asset_rigid_body_count(beta_asset)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*self.base_init_state[:3])
body_names = self.gym.get_asset_rigid_body_names(beta_asset)
self.dof_names = self.gym.get_asset_dof_names(beta_asset)
extremity_name = "FrontalAnkle"
feet_names = [s for s in body_names if extremity_name in s]
self.feet_indices = torch.zeros(
len(feet_names), dtype=torch.long, device=self.device, requires_grad=False
)
knee_names = [s for s in body_names if "Knee" in s]
self.knee_indices = torch.zeros(
len(knee_names), dtype=torch.long, device=self.device, requires_grad=False
)
self.base_index = 0
# add feet force sensors
sensor_pose = gymapi.Transform()
self.gym.create_asset_force_sensor(
beta_asset, self.feet_indices[0], sensor_pose
)
self.gym.create_asset_force_sensor(
beta_asset, self.feet_indices[1], sensor_pose
)
dof_props = self.gym.get_asset_dof_properties(beta_asset)
for i in range(self.num_dof):
dof_props["driveMode"][i] = gymapi.DOF_MODE_POS
dof_props["stiffness"][i] = KP[i]
dof_props["damping"][i] = KD[i] * KD[i]
env_lower = gymapi.Vec3(-spacing, -spacing, 0.0)
env_upper = gymapi.Vec3(spacing, spacing, spacing)
self.beta_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(self.sim, env_lower, env_upper, num_per_row)
eve_beta_handle = self.gym.create_actor(
env_ptr, beta_asset, start_pose, "beta", i, 1, 0
)
self.gym.set_actor_dof_properties(env_ptr, eve_beta_handle, dof_props)
self.gym.enable_actor_dof_force_sensors(env_ptr, eve_beta_handle)
self.envs.append(env_ptr)
self.beta_handles.append(eve_beta_handle)
for i in range(len(feet_names)):
self.feet_indices[i] = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.beta_handles[0], feet_names[i]
)
for i in range(len(knee_names)):
self.knee_indices[i] = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.beta_handles[0], knee_names[i]
)
self.base_index = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.beta_handles[0], "PelvisLink"
)
def pre_physics_step(self, actions):
actions = actions.to(self.rl_device)
modified_actions = actions + KD.to(self.rl_device) * (actions- self.cmd_buffer) / self.dt
self.cmd_buffer = actions.clone()
actions = modified_actions.clone()
actions[self.clocks>=0.5]= actions[ self.clocks>=0.5 ] @ ACTIONS_MIRROR_MAT.to(self.rl_device)
self.actions = actions.clone()
targets = (
self._action_orig_mean
+ self.actions * self._action_orig_dev
+ self.default_dof_pos
)
self.gym.set_dof_position_target_tensor(
self.sim, gymtorch.unwrap_tensor(targets)
)
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
if self.stepper_state % self.push_freq <= self.push_time:
self.push_robots()
elif self.stepper_state % self.push_freq == self.push_time + 1:
self.change_freq = True
if self.change_freq:
self.push_freq = self.original_push_freq * (1 + torch_rand_float(-0.5,0.5,(1,1), device=self.device))
self.push_freq = self.push_freq.clamp(1, 1000).int().item()
self.change_freq = False
print("new push freq ", self.push_freq)
if self.stepper_state % self.log_freq == 0:
print("action sample ", self.actions[0].cpu().numpy() )
print("observation sample ", self.obs_buf[0])
self.compute_observations()
self.compute_reward(self.actions)
self.clocks += self.dt * 5.0 / 8.0
self.clocks %= 1.0
def compute_reward(self, actions):
if not self.use_new_reward:
self.rew_buf[:], (r1, r2, r3) = compute_eve_reward(
self.root_states,
self.commands,
self.torques,
self.rew_scales,
)
if self.stepper_state % self.log_freq == 0:
print(
"partial rewards samples ",
r1.mean().item(),
r2.mean().item(),
r3.mean().item(),
"total reward ",
self.rew_buf.mean().item(),
)
else:
(
self.rew_buf[:],
self.used_quantities,
rewards,
) = compute_eve_cassie_reward(
self.root_states,
self.commands,
self.rigid_body_states,
self.torques,
self.contact_forces,
self.vec_sensor_tensor,
self.clocks,
self.feet_indices,
self.reward_config,
self.used_quantities,
self.first_call,
)
self.first_call = False
if self.stepper_state % self.log_freq == 0:
print("rewards : ")
for key in rewards:
print(f"{key} : {np.round(rewards[key].mean().item(), 3)}", end=" ")
print("total reward ", self.rew_buf.mean().item())
self.reset_buf[:] = compute_resets(
self.contact_forces,
self.base_index,
self.knee_indices,
self.root_states,
self.progress_buf,
self.max_episode_length,
self.rigid_body_states,
self.feet_indices,
)
def push_robots(self):
if(self.stepper_state % self.push_freq == 0):
self.forces = torch.zeros((self.num_envs,13, 3), device=self.device)
self.forces[:,0,:2] = torch_rand_float(-50,50,(self.num_envs, 2), device=self.device)
print(f"pushing robots {self.push_freq}")
self.forces = self.forces.reshape((self.num_envs* 13, 3))
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.forces), None, gymapi.LOCAL_SPACE)
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.stepper_state += 1
self.obs_buf[:]= compute_eve_beta_observation_jiminy(
self.root_states,
self.commands,
self.dof_pos,
self.dof_vel,
self.clocks,
self.vec_sensor_tensor,
self.contact_forces[:, self.feet_indices],
self.contact_wrenches_norm,
self.observation_space_bounds,
OBSERVATION_MIRROR_MAT.to(self.rl_device),
self.gravity_vec,
self.cmd_buffer,
)
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions_offset = torch_rand_float(
0.5, 1.5, (len(env_ids), self.num_dof), device=self.rl_device
)
velocities = torch_rand_float(
-0.1, 0.1, (len(env_ids), self.num_dof), device=self.rl_device
)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset
self.dof_vel[env_ids] = velocities
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.clocks[env_ids] = torch_rand_float(
0, 1, (len(env_ids), 1), device=self.device
).squeeze()
self.clocks[env_ids] -= self.clocks[env_ids] % self.dt
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.commands_x[env_ids] = torch_rand_float(
self.command_x_range[0],
self.command_x_range[1],
(len(env_ids), 1),
device=self.device,
).squeeze()
self.commands_y[env_ids] = torch_rand_float(
self.command_y_range[0],
self.command_y_range[1],
(len(env_ids), 1),
device=self.device,
).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(
self.command_yaw_range[0],
self.command_yaw_range[1],
(len(env_ids), 1),
device=self.device,
).squeeze()
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 1
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def rpy_from_quat(quats):
# takes the quats for the base for each environment as a tensor of size (num_envs, 4) and returns the rpy angles as a tensor of size (num_envs, 3)
rpy = torch.zeros(quats.shape[0], 3, device=quats.device)
rpy[:, 0] = torch.atan2(
2 * (quats[:, 0] * quats[:, 1] + quats[:, 2] * quats[:, 3]),
1 - 2 * (quats[:, 1] * quats[:, 1] + quats[:, 2] * quats[:, 2]),
)
rpy[:, 1] = torch.asin(2 * (quats[:, 0] * quats[:, 2] - quats[:, 3] * quats[:, 1]))
rpy[:, 2] = torch.atan2(
2 * (quats[:, 0] * quats[:, 3] + quats[:, 1] * quats[:, 2]),
1 - 2 * (quats[:, 2] * quats[:, 2] + quats[:, 3] * quats[:, 3]),
)
return rpy
@torch.jit.script
def I_weight_force(x, kappa, phi_stance_start, phi_stance_end):
# Compute Stance Phase
# type: (Tensor, float, float, float) -> Tensor
dphi = (
phi_stance_end - phi_stance_start
if phi_stance_end > phi_stance_start
else 1 - phi_stance_start + phi_stance_end
)
scale = kappa * torch.sin(-torch.pi * ((dphi) + 0.5))
theta = (torch.asin(scale / kappa) / (2 * torch.pi) - phi_stance_end) % 1
return 0.5 * torch.tanh(kappa * torch.sin(2 * torch.pi * (x + theta)) - scale) + 0.5
@torch.jit.script
def normalize_component(x, mini, maxi):
# type: (Tensor, Tensor, Tensor) -> Tensor
return (x - mini) / (maxi - mini)
@torch.jit.script
def compute_eve_reward(
# tensors
root_states,
commands,
torques,
# Dict
rew_scales,
# other
):
# (reward, reset, feet_in air, feet_air_time, episode sums)
# type: (Tensor, Tensor, Tensor, Dict[str, float]) -> Tuple[Tensor , Tuple[Tensor,Tensor,Tensor]]
# Prepare quantities (TODO: return from obs ?)
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
# Velocity tracking reward
lin_vel_error = torch.sum(torch.square(commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error / 0.25) * rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error / 0.25) * rew_scales["ang_vel_z"]
# Torque penalty
rew_torque = torch.sum(torch.square(torques), dim=1) * rew_scales["torque"]
total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_torque
total_reward.clamp_(0.0) # In-place clipping
return total_reward, (rew_lin_vel_xy, rew_ang_vel_z, rew_torque)
@torch.jit.script
def compute_eve_cassie_reward(
# tensors
root_states,
commands,
rigid_body_states,
torques,
contact_forces,
sensor_tensor,
clocks,
feet_indices,
# Dict
reward_config,
# other
used_quantities,
first_call=False,
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Dict[str, Dict[str,Tensor]], Dict[str,Tuple[Tensor,Tensor]], bool) -> Tuple[Tensor, Dict[str,Tuple[Tensor,Tensor]], Dict[str,Tensor]]
base_quat = root_states[:, 3:7]
base_velocity_mean_local = quat_rotate_inverse(base_quat, root_states[:, 7:10])
kappa = reward_config["bipedal"]["kappa_equivalent"]
x = clocks
component_odometry_x = normalize_component(
torch.square(
(base_velocity_mean_local[:, 0] - commands[:, 0])
),
used_quantities["odometry_x"][0],
used_quantities["odometry_x"][1],
)
component_odometry_y =normalize_component(
torch.square(
(base_velocity_mean_local[:, 1] - commands[:, 1])
),
used_quantities["odometry_y"][0],
used_quantities["odometry_y"][1],
)
reward_xy = torch.exp(
- reward_config["factor"]["omega"] * (component_odometry_x + component_odometry_y) / 2.0
)
base_rpy = rpy_from_quat(base_quat)
roll, pitch, yaw = (
base_rpy[:, 0],
base_rpy[:, 1],
base_rpy[:, 2],
)
component_odometry_roll = normalize_component(
torch.square(
roll - reward_config["odometry_roll"]["target"]
),
used_quantities["odometry_roll"][0],
used_quantities["odometry_roll"][1],
)
component_odometry_pitch = normalize_component(
torch.square(
pitch - reward_config["odometry_pitch"]["target"]
),
used_quantities["odometry_pitch"][0],
used_quantities["odometry_pitch"][1],
)
component_odometry_yaw = normalize_component(
torch.square((yaw - commands[:, 2])),
used_quantities["odometry_yaw"][0],
used_quantities["odometry_yaw"][1],
)
reward_orientation = torch.exp(
- reward_config["factor"]["omega"] * (component_odometry_roll + component_odometry_pitch + component_odometry_yaw) / 3.0
)
phi_stance_start_l = reward_config["bipedal"]["theta_left"]
phi_stance_start_r = reward_config["bipedal"]["theta_right"]
stance_times = (
reward_config["bipedal"]["stance_time_left"],
reward_config["bipedal"]["stance_time_right"],
)
left_phase_weight_force = I_weight_force(
x, kappa, phi_stance_start_l, phi_stance_start_l + stance_times[0]
)
left_phase_weight_speed = 1 - left_phase_weight_force
right_phase_weight_force = I_weight_force(
x, kappa, phi_stance_start_r, phi_stance_start_r + stance_times[1]
)
right_phase_weight_speed = 1 - right_phase_weight_force
foot_forces = contact_forces[:, feet_indices].view(-1, 2, 3)
######## Alternating contact reward ########
left_contact_cost = torch.square(sensor_tensor[:, [2]].view(-1))
right_contact_cost = torch.square(sensor_tensor[:, [8]].view(-1))
reward_contact_left = left_phase_weight_force * torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
left_contact_cost,
used_quantities["left_contact_cost"][0],
used_quantities["left_contact_cost"][1],
)
)
reward_contact_right = right_phase_weight_force * torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
right_contact_cost,
used_quantities["right_contact_cost"][0],
used_quantities["right_contact_cost"][1],
)
)
foot_vel = rigid_body_states[:, feet_indices, 7:10]
left_foot_velocity_local = torch.sum(torch.square(foot_vel[:, 0, :]),dim = 1)
right_foot_velocity_local = torch.sum(torch.square(foot_vel[:, 1, :]),dim = 1)
reward_velocity_left = left_phase_weight_speed * torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
left_foot_velocity_local,
used_quantities["velocity_left"][0],
used_quantities["velocity_left"][1],
)
)
reward_velocity_right = right_phase_weight_speed * torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
right_foot_velocity_local,
used_quantities["velocity_right"][0],
used_quantities["velocity_right"][1],
)
)
reward_bipedal = (reward_contact_left + reward_contact_right + reward_velocity_left + reward_velocity_right) / 4.0
reward_torque = - torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
torch.sum(torch.square(torques), dim=1),
used_quantities["torque"][0],
used_quantities["torque"][1],
)
)
reward_friction = torch.exp(
-reward_config["factor"]["omega"]
* normalize_component(
torch.sum(torch.square(foot_forces[:, 0, 0:2]), dim=1),
used_quantities["friction"][0],
used_quantities["friction"][1],
)
)
used_quantities_temp = {
"odometry_x": torch.square(
(base_velocity_mean_local[:, 0] - commands[:, 0])
),
"odometry_y": torch.square(
(base_velocity_mean_local[:, 1] - commands[:, 1])
),
"odometry_roll": torch.square(
roll - reward_config["odometry_roll"]["target"]
),
"odometry_pitch": torch.square(
pitch - reward_config["odometry_pitch"]["target"]
),
"odometry_yaw": torch.square(
yaw - reward_config["odometry_yaw"]["target"]
),
"left_contact_cost": left_contact_cost,
"right_contact_cost": right_contact_cost,
"velocity_left": left_foot_velocity_local,
"velocity_right": right_foot_velocity_local,
"torque": torch.sum(torch.square(torques), dim=1),
"friction": torch.sum(torch.square(foot_forces[:, 0, 0:2]), dim=1),
}
for key in used_quantities_temp:
if not key in used_quantities or first_call:
used_quantities[key] = (
used_quantities_temp[key].min(),
used_quantities_temp[key].max(),
)
used_quantities[key] = (
1.0 * used_quantities_temp[key].min() + 4.0 * used_quantities[key][0]
) / 5.0 if used_quantities_temp[key].min() < used_quantities[key][
0
] else used_quantities[
key
][
0
], (
1.0 * used_quantities_temp[key].max() + 4.0 * used_quantities[key][1]
) / 5.0 if used_quantities_temp[
key
].max() > used_quantities[
key
][
1
] else used_quantities[
key
][
1
]
rewards = {
"reward_xy": reward_xy,
"reward_orientation": reward_orientation,
"bipedal": reward_bipedal,
"torque": reward_torque,
"friction": reward_friction,
}
total_reward = (
20.0 * rewards["reward_xy"] +
4.0 * rewards["reward_orientation"] +
1.0 * rewards["bipedal"] +
1.0 * rewards["torque"] +
1.0 * rewards["friction"]
) / 27.0
weights_sum = torch.tensor(0.0, device=total_reward.device)
for key in rewards:
total_reward += reward_config[key]["weight"] * rewards[key]
weights_sum += reward_config[key]["weight"]
total_reward /= weights_sum
return total_reward, used_quantities, rewards
@torch.jit.script
def compute_resets(
contact_forces,
base_index,
knee_indices,
root_states,
episode_lengths,
max_episode_length,
rigid_body_states,
feet_indices,
):
# type: (Tensor, int, Tensor, Tensor, Tensor, int, Tensor, Tensor) -> Tensor
base_quat = root_states[:, 3:7]
# reset agents
reset = torch.norm(contact_forces[:, base_index, :], dim=1) > 1.0
reset = reset | torch.any(
torch.norm(contact_forces[:, knee_indices, :], dim=2) > 1.0, dim=1
)
# pelvis height can't be lower than 0.4
reset = reset | (root_states[:, 2] < 0.6)
# base_rpy = rpy_from_quat(base_quat)
# # make sure pitch and roll are within 0.3 radians
# reset = (
# reset | (torch.abs(base_rpy[:, 0]) > 0.3) | (torch.abs(base_rpy[:, 1]) > 0.3)
# )
time_out = (
episode_lengths >= max_episode_length - 1
) # no terminal reward for time-outs
reset = reset | time_out
# print("contacts on feet : ", contact_forces[0, feet_indices, :])
feet_pos = rigid_body_states[:, feet_indices, :2]
reset = reset | (
torch.abs(feet_pos[:, 0, 1] - feet_pos[:, 1, 1]) < 0.15
) # | (torch.abs(feet_pos[:, 0, 1] - feet_pos[:, 1, 1]) > 0.5)
# print(f"reward_xy: { gymtorch.unwrap_tensor(rew_lin_vel_xy).mean()} reward_z: {gymtorch.unwrap_tensor(rew_ang_vel_z).mean()} reward_torque: {gymtorch.unwrap_tensor(rew_torque).mean()}")
return reset
@torch.jit.script
def compute_eve_beta_observation_jiminy(
root_states,
commands,
dof_pos,
dof_vel,
clock,
foot_sensors,
foot_forces,
contact_wrenches_norm,
spaces_bounds,
obs_mirror_mat,
gravity_vec,
command_buffer
):
# type: (Tensor, Tensor, Tensor,Tensor,Tensor,Tensor, Tensor ,Tensor, List[Tensor] ,Tensor,Tensor, Tensor) -> Tensor
base_quat = root_states[:, 3:7]
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
# base_rotation = rpy_from_quat(base_quat)
projected_gravity = quat_rotate(base_quat, gravity_vec)
obs = torch.cat(
(
torch.cos(clock.view(-1,1) * 2 * torch.pi),
torch.sin(clock.view(-1,1) * 2 * torch.pi),
projected_gravity.view(-1, 3),
base_ang_vel,
foot_forces[:,0,2].view(-1,1) / contact_wrenches_norm[0],
foot_sensors[:,[3, 4]] / contact_wrenches_norm[1:3],
foot_forces[:,1,2].view(-1,1) / contact_wrenches_norm[3],
foot_sensors[:,[9, 10]] / contact_wrenches_norm[4:6],
dof_pos,
dof_vel,
torch.randn((root_states.shape[0], 12), device=root_states.device),
command_buffer,
commands,
),
dim=-1,
)
# Clipping using a more efficient approach
u = 0
for bound in spaces_bounds:
lower_bound, upper_bound = bound[0], bound[1]
obs[:, u: u + lower_bound.shape[0]].clamp_(lower_bound, upper_bound)
u += lower_bound.shape[0]
# Ensuring symmetry using phase modulation
obs[clock >= 0.5] = obs[clock >= 0.5] @ obs_mirror_mat
return obs