Init

behaviors/custom/reinforcement/walk/walk.py

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+import math
+import os
+import numpy as np
+from behaviors.behavior import Behavior
+from utils.math_ops import MathOps
+from utils.neural_network import run_network, load_network
+from scipy.spatial.transform import Rotation as R
+
+class Walk(Behavior):
+    def __init__(self, agent):
+        super().__init__(agent)
+        self.joint_nominal_position = np.array(
+            [
+                0.0,
+                0.0,
+                0.0,
+                1.4,
+                0.0,
+                -0.4,
+                0.0,
+                -1.4,
+                0.0,
+                0.4,
+                0.0,
+                -0.4,
+                0.0,
+                0.0,
+                0.8,
+                -0.4,
+                0.0,
+                0.4,
+                0.0,
+                0.0,
+                -0.8,
+                0.4,
+                0.0,
+            ]
+        )
+        self.train_sim_flip = np.array(
+            [
+                1.0,
+                -1.0,
+                1.0,
+                -1.0,
+                -1.0,
+                1.0,
+                -1.0,
+                -1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                -1.0,
+                -1.0,
+                1.0,
+                1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+            ]
+        )
+        self.scaling_factor = 0.5
+
+        self.previous_action = np.zeros(len(self.agent.robot.ROBOT_MOTORS))
+
+        self.model = load_network(model_path=os.path.join(os.path.dirname(__file__), "walk.onnx"))
+
+
+    def execute(self, reset: bool, target_2d: list, is_target_absolute: bool, orientation: float=None, is_orientation_absolute: bool=True) -> bool:
+        
+        robot = self.agent.robot
+        world = self.agent.world
+        
+        velocity = None
+
+        if is_target_absolute:
+            raw_target = target_2d - world.global_position[:2]
+            velocity = MathOps.rotate_2d_vec(raw_target, -robot.global_orientation_euler[2], is_rad=False)
+        else:
+            velocity = target_2d
+
+        rel_orientation = None
+        if orientation is None:
+            rel_orientation = MathOps.vector_angle(velocity) * 0.3
+        elif is_orientation_absolute:
+            rel_orientation = MathOps.normalize_deg(orientation - robot.global_orientation_euler[2])
+        else:
+            rel_orientation = orientation * 0.3
+
+        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
+
+        velocity = np.concatenate([velocity, np.array([rel_orientation])], axis=0)
+
+        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
+        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
+
+        radian_joint_positions = np.deg2rad(list(robot.motor_positions.values()))
+        radian_joint_speeds = np.deg2rad(list(robot.motor_speeds.values()))
+
+        qpos_qvel_previous_action = np.vstack(
+            (
+                [
+                    (
+                        (radian_joint_positions * self.train_sim_flip)
+                        - self.joint_nominal_position
+                    )
+                    / 4.6,
+                    radian_joint_speeds / 110.0 * self.train_sim_flip,
+                    self.previous_action / 10.0,
+                ]
+            )
+        ).T.flatten()
+
+        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
+        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
+        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
+        #[0.5,0.25,0.25]
+        observation = np.concatenate(
+            [
+                qpos_qvel_previous_action,
+                ang_vel,
+                velocity,
+                projected_gravity,
+            ]
+        )
+        observation = np.clip(observation, -10.0, 10.0)
+
+        nn_action = run_network(obs=observation, model=self.model)
+
+        target_joint_positions = (
+            self.joint_nominal_position + self.scaling_factor * nn_action
+        )
+        target_joint_positions *= self.train_sim_flip
+
+        self.previous_action = nn_action
+
+        for idx, target in enumerate(target_joint_positions):
+            robot.set_motor_target_position(
+                robot.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
+            )
+
+    def is_ready(self, *args, **kwargs) -> bool:
+        return True