🎨 Use real variables

simulation/requirements.txt

@@ -8,3 +8,7 @@ pybullet
 websockets
 msgpack
 asyncio
+gymnasium
+stable-baselines3[extra]>=2.0.0
+tensorboard
+tqdm

simulation/src/envs/quadruped_env.py

@@ -41,7 +41,7 @@ class QuadrupedRobot:
         position, orientation = p.getBasePositionAndOrientation(self.robot_id)
         orientation = p.getEulerFromQuaternion(orientation)
         velocity, angular_velocity = p.getBaseVelocity(self.robot_id)
-        joint_states = p.getJointStates(self.robot_id, range(p.getNumJoints(self.robot_id)))
+        joint_states = p.getJointStates(self.robot_id, self.movable_joint_indices)
         joint_positions = [state[0] for state in joint_states]
         joint_velocities = [state[1] for state in joint_states]
         return np.concatenate(
@@ -53,7 +53,7 @@ class QuadrupedRobot:
                 joint_positions,
                 joint_velocities,
             ]
-        )
+        ).astype(np.float32)
 
     def apply_action(self, action):
         for i, position in enumerate(action):
@@ -71,29 +71,35 @@ class QuadrupedRobot:
 
 
 class QuadrupedEnv(gym.Env):
-    def __init__(self, terrain_type: TerrainType = TerrainType.FLAT, render_mode: str = "human"):
+    def __init__(
+        self,
+        terrain_type: TerrainType = TerrainType.FLAT,
+        render_mode: str = "human",
+        target_velocity: float = 0.5,
+        max_steps: int = 1000,
+        distance_limit: float = 10.0,
+    ):
         super().__init__()
         if render_mode == "human":
             p.connect(p.GUI)
         else:
             p.connect(p.DIRECT)
 
-        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(48,))
+        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(36,), dtype=np.float32)
-        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(18,))
+        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(12,), dtype=np.float32)
         p.setAdditionalSearchPath(pybullet_data.getDataPath())
 
         self.terrain_type = terrain_type
         self.render_mode = render_mode
-        self.target_velocity = 0.5
+        self.target_velocity = target_velocity
-        self.max_steps = float("inf")
+        self.max_steps = max_steps
         self.current_step = 0
 
         self._setup_world()
         if render_mode == "human":
             self.env_start_state = p.saveState()
 
-        # env parameters
+        self._distance_limit = distance_limit
-        self._distance_limit = float("inf")
 
     def _setup_world(self):
         self.robot = QuadrupedRobot("src/resources/spot.urdf")