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_RL_controller_aslip_nodelta.py
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_RL_controller_aslip_nodelta.py
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from cassie.cassiemujoco.cassieUDP import *
from cassie.cassiemujoco.cassiemujoco_ctypes import *
# from cassie.speed_env import CassieEnv_speed
# from cassie.speed_double_freq_env import CassieEnv_speed_dfreq
# from cassie.speed_no_delta_env import CassieEnv_speed_no_delta
# from cassie.speed_no_delta_neutral_foot_env import CassieEnv_speed_no_delta_neutral_foot
from cassie.trajectory import CassieTrajectory
import time
import numpy as np
import torch
import pickle
import platform
from cassie.quaternion_function import *
#import signal
import atexit
import sys
import select
import tty
import termios
"""
We need to include the trajectory library for the right offset information, as well as the right phaselen and speed
"""
def getAllTrajectories(speeds):
trajectories = []
for i, speed in enumerate(speeds):
dirname = os.path.dirname(__file__)
traj_path = os.path.join(dirname, "cassie", "trajectory", "aslipTrajsSweep", "walkCycle_{}.pkl".format(speed))
trajectories.append(CassieIKTrajectory(traj_path))
# print("Got all trajectories")
return trajectories
class CassieIKTrajectory:
def __init__(self, filepath):
with open(filepath, "rb") as f:
trajectory = pickle.load(f)
self.qpos = np.copy(trajectory["qpos"])
self.length = self.qpos.shape[0]
self.qvel = np.copy(trajectory["qvel"])
self.rfoot = np.copy(trajectory["rfoot"])
self.lfoot = np.copy(trajectory["lfoot"])
# simrate used to be 60
class TrajectoryInfo:
def __init__(self):
self.freq_adjust = 1
self.speeds = [x / 10 for x in range(0, 21)]
self.trajectories = getAllTrajectories(self.speeds)
self.num_speeds = len(self.trajectories)
self.time = 0 # number of time steps in current episode
self.phase = 0 # portion of the phase the robot is in
self.counter = 0 # number of phase cycles completed in episode
# NOTE: each trajectory in trajectories should have the same length
self.speed = self.speeds[0]
self.trajectory = self.trajectories[0]
# NOTE: a reference trajectory represents ONE phase cycle
# should be floor(len(traj) / simrate) - 1
# should be VERY cautious here because wrapping around trajectory
# badly can cause assymetrical/bad gaits
# self.phaselen = floor(self.trajectory.length / self.simrate) - 1
self.phaselen = self.trajectory.length - 1
# see include/cassiemujoco.h for meaning of these indices
self.pos_idx = [7, 8, 9, 14, 20, 21, 22, 23, 28, 34]
self.vel_idx = [6, 7, 8, 12, 18, 19, 20, 21, 25, 31]
# maybe make ref traj only send relevant idxs?
ref_pos, ref_vel = self.get_ref_state(self.phase)
self.offset = ref_pos[self.pos_idx]
self.phase_add = 1
# get the corresponding state from the reference trajectory for the current phase
def get_ref_state(self, phase=None):
if phase is None:
phase = self.phase
if phase > self.phaselen:
phase = 0
# pos = np.copy(self.trajectory.qpos[phase * self.simrate])
pos = np.copy(self.trajectory.qpos[phase])
# this is just setting the x to where it "should" be given the number
# of cycles
#pos[0] += (self.trajectory.qpos[-1, 0] - self.trajectory.qpos[0, 0]) * self.counter
pos[0] += (self.trajectory.qpos[-1, 0] - self.trajectory.qpos[0, 0]) * self.counter
# ^ should only matter for COM error calculation,
# gets dropped out of state variable for input reasons
# setting lateral distance target to 0?
# regardless of reference trajectory?
pos[1] = 0
# vel = np.copy(self.trajectory.qvel[phase * self.simrate])
vel = np.copy(self.trajectory.qvel[phase])
return pos, vel
def update_info(self, new_speed):
self.speed = new_speed
# find closest speed in [0.0, 0.1, ... 3.0]. use this to find new trajectory
self.trajectory = self.trajectories[ (np.abs([speed_i - self.speed for speed_i in self.speeds])).argmin() ]
# new offset
ref_pos, ref_vel = self.get_ref_state(self.phase)
self.offset = ref_pos[self.pos_idx]
# phaselen
self.phaselen = self.trajectory.length - 1
return self.phaselen, self.offset
time_log = [] # time stamp
input_log = [] # network inputs
output_log = [] # network outputs
state_log = [] # cassie state
target_log = [] #PD target log
filename = "test.p"
filep = open(filename, "wb")
max_speed = 3.0
min_speed = 0.0
def log():
data = {"time": time_log, "output": output_log, "input": input_log, "state": state_log, "target": target_log}
pickle.dump(data, filep)
def isData():
return select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], [])
atexit.register(log)
# Prevent latency issues by disabling multithreading in pytorch
torch.set_num_threads(1)
# Prepare model
# env = CassieEnv_speed_no_delta_neutral_foot("walking", clock_based=True, state_est=True)
# env.reset_for_test()
traj = TrajectoryInfo()
# policy = torch.load("./trained_models/aslip_unified_freq_correction.pt")
policy = torch.load("./trained_models/aslip_unified_no_delta.pt")
policy.eval()
max_speed = 3.0
min_speed = 0.0
max_y_speed = 0.5
min_y_speed = -0.5
symmetry = True
# Initialize control structure with gains
P = np.array([100, 100, 88, 96, 50, 100, 100, 88, 96, 50])
D = np.array([10.0, 10.0, 8.0, 9.6, 5.0, 10.0, 10.0, 8.0, 9.6, 5.0])
u = pd_in_t()
for i in range(5):
u.leftLeg.motorPd.pGain[i] = P[i]
u.leftLeg.motorPd.dGain[i] = D[i]
u.rightLeg.motorPd.pGain[i] = P[i+5]
u.rightLeg.motorPd.dGain[i] = D[i+5]
pos_index = np.array([2,3,4,5,6,7,8,9,14,15,16,20,21,22,23,28,29,30,34])
vel_index = np.array([0,1,2,3,4,5,6,7,8,12,13,14,18,19,20,21,25,26,27,31])
pos_mirror_index = np.array([2,3,4,5,6,21,22,23,28,29,30,34,7,8,9,14,15,16,20])
vel_mirror_index = np.array([0,1,2,3,4,5,19,20,21,25,26,27,31,6,7,8,12,13,14,18])
offset = traj.offset
# offset = np.array([0.0045, 0.0, 0.4973, -1.1997, -1.5968, 0.0045, 0.0, 0.4973, -1.1997, -1.5968])
# Determine whether running in simulation or on the robot
if platform.node() == 'cassie':
cassie = CassieUdp(remote_addr='10.10.10.3', remote_port='25010',
local_addr='10.10.10.100', local_port='25011')
else:
cassie = CassieUdp() # local testing
# Connect to the simulator or robot
print('Connecting...')
y = None
while y is None:
cassie.send_pd(pd_in_t())
time.sleep(0.001)
y = cassie.recv_newest_pd()
received_data = True
print('Connected!\n')
# Record time
t = time.monotonic()
t0 = t
orient_add = 0
old_settings = termios.tcgetattr(sys.stdin)
try:
tty.setcbreak(sys.stdin.fileno())
while True:
# Wait until next cycle time
while time.monotonic() - t < 60/2000:
time.sleep(0.001)
t = time.monotonic()
tt = time.monotonic() - t0
# Get newest state
state = cassie.recv_newest_pd()
if state is None:
print('Missed a cycle')
continue
if platform.node() == 'cassie':
# Radio control
orient_add -= state.radio.channel[3] / 60.0
traj.speed = max(min_speed, state.radio.channel[0] * max_speed)
traj.speed = min(max_speed, state.radio.channel[0] * max_speed)
traj.phase_add = state.radio.channel[5] + 1
# env.y_speed = max(min_y_speed, -state.radio.channel[1] * max_y_speed)
# env.y_speed = min(max_y_speed, -state.radio.channel[1] * max_y_speed)
else:
# Automatically change orientation and speed
tt = time.monotonic() - t0
orient_add += 0#math.sin(t / 8) / 400
if isData():
c = sys.stdin.read(1)
if c == 'w':
traj.speed += .1
print("Increasing speed to: ", traj.speed)
elif c == 's':
traj.speed -= .1
print("Decreasing speed to: ", traj.speed)
# elif c == 'a':
# y_speed += .1
# print("Increasing y speed to: ", y_speed)
# elif c == 'd':
# y_speed -= .1
# print("Decreasing y speed to: ", y_speed)
elif c == 'j':
traj.freq_adjust += .1
print("Increasing frequency to: ", traj.freq_adjust)
elif c == 'h':
traj.freq_adjust -= .1
print("Decreasing frequency to: ", traj.freq_adjust)
elif c == 'l':
orient_add += .1
print("Increasing orient_add to: ", orient_add)
elif c == 'k':
orient_add -= .1
print("Decreasing orient_add to: ", orient_add)
elif c == 'p':
print("Applying force")
push = 100
push_dir = 0
force_arr = np.zeros(6)
force_arr[push_dir] = push
env.sim.apply_force(force_arr)
else:
pass
traj.speed = max(min_speed, traj.speed)
traj.speed = min(max_speed, traj.speed)
# y_speed = max(min_y_speed, y_speed)
# y_speed = min(max_y_speed, y_speed)
# print("y_speed: ", y_speed)
# print("frequency: ", traj.phase_add)
traj.update_info(traj.speed)
clock = [np.sin(2 * np.pi * traj.phase * traj.freq_adjust / traj.phaselen), np.cos(2 * np.pi * traj.phase * traj.freq_adjust / traj.phaselen)]
# euler_orient = quaternion2euler(state.pelvis.orientation[:])
# print("euler orient: ", euler_orient + np.array([orient_add, 0, 0]))
# new_orient = euler2quat(euler_orient + np.array([orient_add, 0, 0]))
quaternion = euler2quat(z=orient_add, y=0, x=0)
iquaternion = inverse_quaternion(quaternion)
new_orient = quaternion_product(iquaternion, state.pelvis.orientation[:])
if new_orient[0] < 0:
new_orient = -new_orient
new_translationalVelocity = rotate_by_quaternion(state.pelvis.translationalVelocity[:], iquaternion)
# print('new_orientation: {}'.format(new_orient))
# ext_state = np.concatenate((clock, [speed, y_speed]))
ext_state = np.concatenate((clock, [traj.speed] ))
robot_state = np.concatenate([
[state.pelvis.position[2] - state.terrain.height], # pelvis height
new_orient,
# state.pelvis.orientation[:], # pelvis orientation
state.motor.position[:], # actuated joint positions
state.pelvis.translationalVelocity[:], # pelvis translational velocity
# new_translationalVelocity[:],
state.pelvis.rotationalVelocity[:], # pelvis rotational velocity
state.motor.velocity[:], # actuated joint velocities
state.pelvis.translationalAcceleration[:], # pelvis translational acceleration
state.joint.position[:], # unactuated joint positions
state.joint.velocity[:] # unactuated joint velocities
])
RL_state = np.concatenate([robot_state, ext_state])
actual_speed = state.pelvis.translationalVelocity[0]
print("target speed: {:.2f}\tactual speed: {:.2f}\tfreq: {}".format(traj.speed, actual_speed, traj.freq_adjust))
#pretending the height is always 1.0
# RL_state[0] = 1.0
# Construct input vector
torch_state = torch.Tensor(RL_state)
# torch_state = shared_obs_stats.normalize(torch_state)
# Get action
action = policy.act(torch_state, True)
env_action = action.data.numpy()
target = env_action
# target = env_action + traj.offset
#print(state.pelvis.position[2] - state.terrain.height)
# Send action
for i in range(5):
u.leftLeg.motorPd.pTarget[i] = target[i]
u.rightLeg.motorPd.pTarget[i] = target[i+5]
#time.sleep(0.005)
cassie.send_pd(u)
# Measure delay
print('delay: {:6.1f} ms'.format((time.monotonic() - t) * 1000))
# Logging
time_log.append(time.time())
state_log.append(state)
input_log.append(RL_state)
output_log.append(env_action)
target_log.append(target)
# Track phase
traj.phase += traj.phase_add
if traj.phase >= traj.phaselen:
traj.phase = 0
traj.counter += 1
finally:
termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)