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https://github.com/bulletphysics/bullet3
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322 lines
12 KiB
Python
322 lines
12 KiB
Python
from robot_bases import XmlBasedRobot, MJCFBasedRobot, URDFBasedRobot
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import numpy as np
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import pybullet
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import os
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import pybullet_data
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from robot_bases import BodyPart
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class WalkerBase(MJCFBasedRobot):
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def __init__(self, fn, robot_name, action_dim, obs_dim, power):
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MJCFBasedRobot.__init__(self, fn, robot_name, action_dim, obs_dim)
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self.power = power
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self.camera_x = 0
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self.start_pos_x, self.start_pos_y, self.start_pos_z = 0, 0, 0
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self.walk_target_x = 1e3 # kilometer away
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self.walk_target_y = 0
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self.body_xyz=[0,0,0]
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def robot_specific_reset(self, bullet_client):
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self._p = bullet_client
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for j in self.ordered_joints:
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j.reset_current_position(self.np_random.uniform(low=-0.1, high=0.1), 0)
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self.feet = [self.parts[f] for f in self.foot_list]
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self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32)
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self.scene.actor_introduce(self)
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self.initial_z = None
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def apply_action(self, a):
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assert (np.isfinite(a).all())
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for n, j in enumerate(self.ordered_joints):
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j.set_motor_torque(self.power * j.power_coef * float(np.clip(a[n], -1, +1)))
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def calc_state(self):
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j = np.array([j.current_relative_position() for j in self.ordered_joints], dtype=np.float32).flatten()
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# even elements [0::2] position, scaled to -1..+1 between limits
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# odd elements [1::2] angular speed, scaled to show -1..+1
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self.joint_speeds = j[1::2]
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self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99)
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body_pose = self.robot_body.pose()
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parts_xyz = np.array([p.pose().xyz() for p in self.parts.values()]).flatten()
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self.body_xyz = (
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parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2]) # torso z is more informative than mean z
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self.body_rpy = body_pose.rpy()
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z = self.body_xyz[2]
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if self.initial_z == None:
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self.initial_z = z
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r, p, yaw = self.body_rpy
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self.walk_target_theta = np.arctan2(self.walk_target_y - self.body_xyz[1],
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self.walk_target_x - self.body_xyz[0])
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self.walk_target_dist = np.linalg.norm(
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[self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]])
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angle_to_target = self.walk_target_theta - yaw
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rot_speed = np.array(
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[[np.cos(-yaw), -np.sin(-yaw), 0],
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[np.sin(-yaw), np.cos(-yaw), 0],
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[ 0, 0, 1]]
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)
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vx, vy, vz = np.dot(rot_speed, self.robot_body.speed()) # rotate speed back to body point of view
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more = np.array([ z-self.initial_z,
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np.sin(angle_to_target), np.cos(angle_to_target),
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0.3* vx , 0.3* vy , 0.3* vz , # 0.3 is just scaling typical speed into -1..+1, no physical sense here
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r, p], dtype=np.float32)
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return np.clip( np.concatenate([more] + [j] + [self.feet_contact]), -5, +5)
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def calc_potential(self):
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# progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second),
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# all rewards have rew/frame units and close to 1.0
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debugmode=0
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if (debugmode):
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print("calc_potential: self.walk_target_dist")
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print(self.walk_target_dist)
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print("self.scene.dt")
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print(self.scene.dt)
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print("self.scene.frame_skip")
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print(self.scene.frame_skip)
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print("self.scene.timestep")
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print(self.scene.timestep)
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return - self.walk_target_dist / self.scene.dt
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class Hopper(WalkerBase):
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foot_list = ["foot"]
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def __init__(self):
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WalkerBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75)
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def alive_bonus(self, z, pitch):
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return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
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class Walker2D(WalkerBase):
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foot_list = ["foot", "foot_left"]
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def __init__(self):
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WalkerBase.__init__(self, "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40)
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def alive_bonus(self, z, pitch):
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return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
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def robot_specific_reset(self, bullet_client):
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WalkerBase.robot_specific_reset(self, bullet_client)
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for n in ["foot_joint", "foot_left_joint"]:
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self.jdict[n].power_coef = 30.0
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class HalfCheetah(WalkerBase):
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foot_list = ["ffoot", "fshin", "fthigh", "bfoot", "bshin", "bthigh"] # track these contacts with ground
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def __init__(self):
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WalkerBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90)
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def alive_bonus(self, z, pitch):
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# Use contact other than feet to terminate episode: due to a lot of strange walks using knees
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return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[2] and not self.feet_contact[4] and not self.feet_contact[5] else -1
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def robot_specific_reset(self, bullet_client):
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WalkerBase.robot_specific_reset(self, bullet_client)
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self.jdict["bthigh"].power_coef = 120.0
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self.jdict["bshin"].power_coef = 90.0
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self.jdict["bfoot"].power_coef = 60.0
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self.jdict["fthigh"].power_coef = 140.0
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self.jdict["fshin"].power_coef = 60.0
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self.jdict["ffoot"].power_coef = 30.0
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class Ant(WalkerBase):
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foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot']
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def __init__(self):
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WalkerBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=2.5)
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def alive_bonus(self, z, pitch):
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return +1 if z > 0.26 else -1 # 0.25 is central sphere rad, die if it scrapes the ground
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class Humanoid(WalkerBase):
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self_collision = True
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foot_list = ["right_foot", "left_foot"] # "left_hand", "right_hand"
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def __init__(self):
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WalkerBase.__init__(self, 'humanoid_symmetric.xml', 'torso', action_dim=17, obs_dim=44, power=0.41)
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# 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25
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def robot_specific_reset(self, bullet_client):
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WalkerBase.robot_specific_reset(self, bullet_client)
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self.motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
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self.motor_power = [100, 100, 100]
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self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
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self.motor_power += [100, 100, 300, 200]
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self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
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self.motor_power += [100, 100, 300, 200]
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self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"]
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self.motor_power += [75, 75, 75]
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self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
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self.motor_power += [75, 75, 75]
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self.motors = [self.jdict[n] for n in self.motor_names]
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if self.random_yaw:
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position = [0,0,0]
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orientation = [0,0,0]
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yaw = self.np_random.uniform(low=-3.14, high=3.14)
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if self.random_lean and self.np_random.randint(2)==0:
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cpose.set_xyz(0, 0, 1.4)
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if self.np_random.randint(2)==0:
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pitch = np.pi/2
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position = [0, 0, 0.45]
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else:
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pitch = np.pi*3/2
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position = [0, 0, 0.25]
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roll = 0
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orientation = [roll, pitch, yaw]
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else:
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position = [0, 0, 1.4]
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orientation = [0, 0, yaw] # just face random direction, but stay straight otherwise
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self.robot_body.reset_position(position)
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self.robot_body.reset_orientation(orientation)
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self.initial_z = 0.8
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random_yaw = False
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random_lean = False
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def apply_action(self, a):
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assert( np.isfinite(a).all() )
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force_gain = 1
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for i, m, power in zip(range(17), self.motors, self.motor_power):
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m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1)))
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def alive_bonus(self, z, pitch):
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return +2 if z > 0.78 else -1 # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying
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def get_cube(_p, x, y, z):
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body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"cube_small.urdf"), [x, y, z])
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_p.changeDynamics(body,-1, mass=1.2)#match Roboschool
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part_name, _ = _p.getBodyInfo(body)
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part_name = part_name.decode("utf8")
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bodies = [body]
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return BodyPart(_p, part_name, bodies, 0, -1)
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def get_sphere(_p, x, y, z):
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body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"sphere2red_nocol.urdf"), [x, y, z])
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part_name, _ = _p.getBodyInfo(body)
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part_name = part_name.decode("utf8")
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bodies = [body]
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return BodyPart(_p, part_name, bodies, 0, -1)
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class HumanoidFlagrun(Humanoid):
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def __init__(self):
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Humanoid.__init__(self)
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self.flag = None
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def robot_specific_reset(self, bullet_client):
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Humanoid.robot_specific_reset(self, bullet_client)
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self.flag_reposition()
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def flag_reposition(self):
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self.walk_target_x = self.np_random.uniform(low=-self.scene.stadium_halflen, high=+self.scene.stadium_halflen)
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self.walk_target_y = self.np_random.uniform(low=-self.scene.stadium_halfwidth, high=+self.scene.stadium_halfwidth)
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more_compact = 0.5 # set to 1.0 whole football field
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self.walk_target_x *= more_compact
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self.walk_target_y *= more_compact
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if (self.flag):
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#for b in self.flag.bodies:
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# print("remove body uid",b)
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# p.removeBody(b)
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self._p.resetBasePositionAndOrientation(self.flag.bodies[0],[self.walk_target_x, self.walk_target_y, 0.7],[0,0,0,1])
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else:
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self.flag = get_sphere(self._p, self.walk_target_x, self.walk_target_y, 0.7)
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self.flag_timeout = 600/self.scene.frame_skip #match Roboschool
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def calc_state(self):
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self.flag_timeout -= 1
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state = Humanoid.calc_state(self)
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if self.walk_target_dist < 1 or self.flag_timeout <= 0:
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self.flag_reposition()
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state = Humanoid.calc_state(self) # caclulate state again, against new flag pos
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self.potential = self.calc_potential() # avoid reward jump
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return state
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class HumanoidFlagrunHarder(HumanoidFlagrun):
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def __init__(self):
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HumanoidFlagrun.__init__(self)
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self.flag = None
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self.aggressive_cube = None
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self.frame = 0
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def robot_specific_reset(self, bullet_client):
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HumanoidFlagrun.robot_specific_reset(self, bullet_client)
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self.frame = 0
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if (self.aggressive_cube):
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self._p.resetBasePositionAndOrientation(self.aggressive_cube.bodies[0],[-1.5,0,0.05],[0,0,0,1])
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else:
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self.aggressive_cube = get_cube(self._p, -1.5,0,0.05)
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self.on_ground_frame_counter = 0
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self.crawl_start_potential = None
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self.crawl_ignored_potential = 0.0
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self.initial_z = 0.8
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def alive_bonus(self, z, pitch):
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if self.frame%30==0 and self.frame>100 and self.on_ground_frame_counter==0:
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target_xyz = np.array(self.body_xyz)
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robot_speed = np.array(self.robot_body.speed())
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angle = self.np_random.uniform(low=-3.14, high=3.14)
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from_dist = 4.0
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attack_speed = self.np_random.uniform(low=20.0, high=30.0) # speed 20..30 (* mass in cube.urdf = impulse)
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time_to_travel = from_dist / attack_speed
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target_xyz += robot_speed*time_to_travel # predict future position at the moment the cube hits the robot
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position = [target_xyz[0] + from_dist*np.cos(angle),
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target_xyz[1] + from_dist*np.sin(angle),
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target_xyz[2] + 1.0]
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attack_speed_vector = target_xyz - np.array(position)
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attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector)
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attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,))
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self.aggressive_cube.reset_position(position)
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self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector)
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if z < 0.8:
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self.on_ground_frame_counter += 1
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elif self.on_ground_frame_counter > 0:
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self.on_ground_frame_counter -= 1
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# End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations.
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self.frame += 1
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return self.potential_leak() if self.on_ground_frame_counter<170 else -1
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def potential_leak(self):
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z = self.body_xyz[2] # 0.00 .. 0.8 .. 1.05 normal walk, 1.2 when jumping
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z = np.clip(z, 0, 0.8)
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return z/0.8 + 1.0 # 1.00 .. 2.0
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def calc_potential(self):
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# We see alive bonus here as a leak from potential field. Value V(s) of a given state equals
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# potential, if it is topped up with gamma*potential every frame. Gamma is assumed 0.99.
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#
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# 2.0 alive bonus if z>0.8, potential is 200, leak gamma=0.99, (1-0.99)*200==2.0
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# 1.0 alive bonus on the ground z==0, potential is 100, leak (1-0.99)*100==1.0
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#
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# Why robot whould stand up: to receive 100 points in potential field difference.
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flag_running_progress = Humanoid.calc_potential(self)
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# This disables crawl.
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if self.body_xyz[2] < 0.8:
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if self.crawl_start_potential is None:
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self.crawl_start_potential = flag_running_progress - self.crawl_ignored_potential
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#print("CRAWL START %+0.1f %+0.1f" % (self.crawl_start_potential, flag_running_progress))
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self.crawl_ignored_potential = flag_running_progress - self.crawl_start_potential
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flag_running_progress = self.crawl_start_potential
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else:
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#print("CRAWL STOP %+0.1f %+0.1f" % (self.crawl_ignored_potential, flag_running_progress))
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flag_running_progress -= self.crawl_ignored_potential
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self.crawl_start_potential = None
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return flag_running_progress + self.potential_leak()*100
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