from agent.Base_Agent import Base_Agent from behaviors.custom.Step.Step_Generator import Step_Generator from math_ops.Math_Ops import Math_Ops as M import math import numpy as np class Env(): def __init__(self, base_agent : Base_Agent) -> None: self.world = base_agent.world self.ik = base_agent.inv_kinematics # State space self.obs = np.zeros(63, np.float32) # Step behavior defaults self.STEP_DUR = 8 self.STEP_Z_SPAN = 0.02 self.STEP_Z_MAX = 0.70 # IK nao_specs = self.ik.NAO_SPECS self.leg_length = nao_specs[1] + nao_specs[3] # upper leg height + lower leg height feet_y_dev = nao_specs[0] * 1.12 # wider step sample_time = self.world.robot.STEPTIME max_ankle_z = nao_specs[5] self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z) self.DEFAULT_ARMS = np.array([-90,-90,8,8,90,90,70,70],np.float32) self.walk_rel_orientation = None self.walk_rel_target = None self.walk_distance = None def observe(self, init=False): r = self.world.robot if init: # reset variables self.act = np.zeros(16, np.float32) # memory variable self.step_counter = 0 # index observation naive normalization self.obs[0] = min(self.step_counter,15*8) /100 # simple counter: 0,1,2,3... self.obs[1] = r.loc_head_z *3 # z coordinate (torso) self.obs[2] = r.loc_head_z_vel /2 # z velocity (torso) self.obs[3] = r.imu_torso_roll /15 # absolute torso roll in deg self.obs[4] = r.imu_torso_pitch /15 # absolute torso pitch in deg self.obs[5:8] = r.gyro /100 # gyroscope self.obs[8:11] = r.acc /10 # accelerometer self.obs[11:17] = r.frp.get('lf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)* self.obs[17:23] = r.frp.get('rf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)* # *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0) # Joints: Forward kinematics for ankles + feet rotation + arms (pitch + roll) rel_lankle = self.ik.get_body_part_pos_relative_to_hip("lankle") # ankle position relative to center of both hip joints rel_rankle = self.ik.get_body_part_pos_relative_to_hip("rankle") # ankle position relative to center of both hip joints lf = r.head_to_body_part_transform("torso", r.body_parts['lfoot'].transform ) # foot transform relative to torso rf = r.head_to_body_part_transform("torso", r.body_parts['rfoot'].transform ) # foot transform relative to torso lf_rot_rel_torso = np.array( [lf.get_roll_deg(), lf.get_pitch_deg(), lf.get_yaw_deg()] ) # foot rotation relative to torso rf_rot_rel_torso = np.array( [rf.get_roll_deg(), rf.get_pitch_deg(), rf.get_yaw_deg()] ) # foot rotation relative to torso # pose self.obs[23:26] = rel_lankle * (8,8,5) self.obs[26:29] = rel_rankle * (8,8,5) self.obs[29:32] = lf_rot_rel_torso / 20 self.obs[32:35] = rf_rot_rel_torso / 20 self.obs[35:39] = r.joints_position[14:18] /100 # arms (pitch + roll) # velocity self.obs[39:55] = r.joints_target_last_speed[2:18] # predictions == last action ''' Expected observations for walking state: Time step R 0 1 2 3 4 5 6 7 0 Progress 1 0 .14 .28 .43 .57 .71 .86 1 0 Left leg active T F F F F F F F F T ''' if init: # the walking parameters refer to the last parameters in effect (after a reset, they are pointless) self.obs[55] = 1 # step progress self.obs[56] = 1 # 1 if left leg is active self.obs[57] = 0 # 1 if right leg is active else: self.obs[55] = self.step_generator.external_progress # step progress self.obs[56] = float(self.step_generator.state_is_left_active) # 1 if left leg is active self.obs[57] = float(not self.step_generator.state_is_left_active) # 1 if right leg is active ''' Create internal target with a smoother variation ''' MAX_LINEAR_DIST = 0.5 MAX_LINEAR_DIFF = 0.014 # max difference (meters) per step MAX_ROTATION_DIFF = 1.6 # max difference (degrees) per step MAX_ROTATION_DIST = 45 if init: self.internal_rel_orientation = 0 self.internal_target = np.zeros(2) previous_internal_target = np.copy(self.internal_target) #---------------------------------------------------------------- compute internal linear target rel_raw_target_size = np.linalg.norm(self.walk_rel_target) if rel_raw_target_size == 0: rel_target = self.walk_rel_target else: rel_target = self.walk_rel_target / rel_raw_target_size * min(self.walk_distance, MAX_LINEAR_DIST) internal_diff = rel_target - self.internal_target internal_diff_size = np.linalg.norm(internal_diff) if internal_diff_size > MAX_LINEAR_DIFF: self.internal_target += internal_diff * (MAX_LINEAR_DIFF / internal_diff_size) else: self.internal_target[:] = rel_target #---------------------------------------------------------------- compute internal rotation target internal_ori_diff = np.clip( M.normalize_deg( self.walk_rel_orientation - self.internal_rel_orientation ), -MAX_ROTATION_DIFF, MAX_ROTATION_DIFF) self.internal_rel_orientation = np.clip(M.normalize_deg( self.internal_rel_orientation + internal_ori_diff ), -MAX_ROTATION_DIST, MAX_ROTATION_DIST) #----------------------------------------------------------------- observations internal_target_vel = self.internal_target - previous_internal_target self.obs[58] = self.internal_target[0] / MAX_LINEAR_DIST self.obs[59] = self.internal_target[1] / MAX_LINEAR_DIST self.obs[60] = self.internal_rel_orientation / MAX_ROTATION_DIST self.obs[61] = internal_target_vel[0] / MAX_LINEAR_DIFF self.obs[62] = internal_target_vel[0] / MAX_LINEAR_DIFF return self.obs def execute_ik(self, l_pos, l_rot, r_pos, r_rot): r = self.world.robot # Apply IK to each leg + Set joint targets # Left leg indices, self.values_l, error_codes = self.ik.leg(l_pos, l_rot, True, dynamic_pose=False) r.set_joints_target_position_direct(indices, self.values_l, harmonize=False) # Right leg indices, self.values_r, error_codes = self.ik.leg(r_pos, r_rot, False, dynamic_pose=False) r.set_joints_target_position_direct(indices, self.values_r, harmonize=False) def execute(self, action): r = self.world.robot # Actions: # 0,1,2 left ankle pos # 3,4,5 right ankle pos # 6,7,8 left foot rotation # 9,10,11 right foot rotation # 12,13 left/right arm pitch # 14,15 left/right arm roll internal_dist = np.linalg.norm( self.internal_target ) action_mult = 1 if internal_dist > 0.2 else (0.7/0.2) * internal_dist + 0.3 # exponential moving average self.act = 0.8 * self.act + 0.2 * action * action_mult * 0.7 # execute Step behavior to extract the target positions of each leg (we will override these targets) lfy,lfz,rfy,rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR, self.STEP_Z_SPAN, self.leg_length * self.STEP_Z_MAX) # Leg IK a = self.act l_ankle_pos = (a[0]*0.02, max(0.01, a[1]*0.02 + lfy), a[2]*0.01 + lfz) # limit y to avoid self collision r_ankle_pos = (a[3]*0.02, min(a[4]*0.02 + rfy, -0.01), a[5]*0.01 + rfz) # limit y to avoid self collision l_foot_rot = a[6:9] * (3,3,5) r_foot_rot = a[9:12] * (3,3,5) # Limit leg yaw/pitch l_foot_rot[2] = max(0,l_foot_rot[2] + 7) r_foot_rot[2] = min(0,r_foot_rot[2] - 7) # Arms actions arms = np.copy(self.DEFAULT_ARMS) # default arms pose arm_swing = math.sin(self.step_generator.state_current_ts / self.STEP_DUR * math.pi) * 6 inv = 1 if self.step_generator.state_is_left_active else -1 arms[0:4] += a[12:16]*4 + (-arm_swing*inv,arm_swing*inv,0,0) # arms pitch+roll # Set target positions self.execute_ik(l_ankle_pos, l_foot_rot, r_ankle_pos, r_foot_rot) # legs r.set_joints_target_position_direct( slice(14,22), arms, harmonize=False ) # arms self.step_counter += 1