learning tweaks

Robot.py

@@ -265,10 +265,8 @@ class Robot:
         self.set_joint_angles(target_rad)
 
     def apply_rl_action(self, action: np.ndarray) -> None:
-        """Applies absolute action targets directly for Direct RL."""
         action = np.asarray(action, dtype=np.float32)
-        scaled_action = np.clip(action, -1.0, 1.0) * (np.pi / 2.0)
+        new_rad = dt.RadArray(data=action.reshape(self.current_rad.data.shape))
-        new_rad = dt.RadArray(data=scaled_action.reshape(self.current_rad.data.shape))
         self.set_joint_angles(new_rad)
 
     def apply_rl_action_delta(self, action: np.ndarray) -> None:

ml/env.py

@@ -81,9 +81,11 @@ class JackBotEnv(gym.Env):
 
         self.action_space = spaces.Box(-1.0, 1.0, shape=(action_dim,), dtype=np.float32)
         self.observation_space = spaces.Box(-np.inf, np.inf, shape=(obs_dim,), dtype=np.float32)
+        self.min_joint_limits, self.max_joint_limits = self.sim._get_urdf_joint_limits()
 
         self.command = np.zeros(3, dtype=np.float32)  # [vx, vy, omega]
         self.last_action = np.zeros(action_dim, dtype=np.float32)
+        self.last_last_action = np.zeros(action_dim, dtype=np.float32)
         self.target_height = 0.122
         self.collapse_height_fraction = 0.55
         self.tilt_failure_rad = 0.9
@@ -143,7 +145,7 @@ class JackBotEnv(gym.Env):
         self.last_reward_components = {}
         self.episode_reward_components_sum = defaultdict(float)
 
-        spawn_pos = [0.0, 0.0, 0.20]
+        spawn_pos = [0.0, 0.0, 0.15]
         spawn_orn = [0.0, 0.0, 0.0, 1.0]
 
         # 1. Reset base pose and velocities
@@ -155,7 +157,9 @@ class JackBotEnv(gym.Env):
         if self.use_gui:
             self.sim.set_robot_color([1.0, 1.0, 1.0, 1.0])
 
-        self.last_action = np.zeros(self.action_space.shape[0], dtype=np.float32)
+        action_dim = self.action_space.shape[0]
+        self.last_action = np.zeros(action_dim, dtype=np.float32)
+        self.last_last_action = np.zeros(action_dim, dtype=np.float32)
         self.max_distance_from_start = 0.0
         self.max_survival_steps = 0
 
@@ -170,8 +174,13 @@ class JackBotEnv(gym.Env):
         self.start_position = list(pos)
 
         # Drop settlement
-        self.target_height = self.sim.settle_and_measure_height(steps=200, fallback_height=0.122)
+        self.target_height = self.sim.settle_and_measure_height(
+            target_angles=self.robot.current_rad, 
+            steps=300, 
+            fallback_height=0.122
+        )
         self.default_joint_angles = self.sim.get_robot_joint_angles()
+        self.default_action = np.clip(self.default_joint_angles / (np.pi / 2.0), -1.0, 1.0)
 
         if self.use_gui:
             self.hud.reset()
@@ -183,9 +192,11 @@ class JackBotEnv(gym.Env):
         return self.robot.get_observation(command=self.command)
 
     def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
+        previous_action = self.last_action.copy()
         self.step_count += 1
         self.total_steps += 1
-        previous_action = self.last_action.copy()
+
+        self.last_last_action = self.last_action.copy()
         self.last_action = action.copy()
 
         # Command resampling
@@ -200,9 +211,14 @@ class JackBotEnv(gym.Env):
         # Mirror main.py input resolution logic: update robot_state and vector_dirmov directly
         self.robot.robot_state = "walking" if (abs(cmd_vx) > 0.01 or abs(cmd_vy) > 0.01 or abs(cmd_omega) > 0.01) else "idle"
         self.robot.vector_dirmov = [float(cmd_vx), float(cmd_vy), float(cmd_omega)]
-
+        
         # Delegate execution tick to Robot instance
-        self.robot.tick(action=action)
+        target_angles = np.where(
+            action < 0.0,
+            self.default_joint_angles + action * (self.default_joint_angles - self.min_joint_limits),
+            self.default_joint_angles + action * (self.max_joint_limits - self.default_joint_angles)
+        )
+        self.robot.tick(action=target_angles)
 
         if self.robot_mode != "kinematics" and self.step_count % 60 == 0:
             random_force = np.random.uniform(-2.0, 2.0, size=2)
@@ -265,9 +281,8 @@ class JackBotEnv(gym.Env):
 
     def _update_curriculum(self):
         self._curriculum_advanced = False
-        forced_forward = (self.curriculum_phase < CurriculumPhase.FORWARD) and (self.step_count >= 2500)
 
-        if self.curriculum_phase < CurriculumPhase.FORWARD and (self._phase_progress_ready(CurriculumPhase.FORWARD) or forced_forward):
+        if self.curriculum_phase < CurriculumPhase.FORWARD and (self._phase_progress_ready(CurriculumPhase.FORWARD)):
             self.curriculum_phase = CurriculumPhase.FORWARD
             self._curriculum_advanced = True
         elif self.curriculum_phase < CurriculumPhase.TURN_AND_DIRECTION and self._phase_progress_ready(CurriculumPhase.TURN_AND_DIRECTION):
@@ -289,12 +304,19 @@ class JackBotEnv(gym.Env):
         cmd_norm = math.hypot(cmd_vx, cmd_vy)
 
         raw_speed = math.hypot(linear_vel[0], linear_vel[1])
+        # Forgiving zones: ignore noise below 0.04 m/s and 0.05 rad/s
         filtered_vx, filtered_vy = (linear_vel[0], linear_vel[1]) if raw_speed >= 0.04 else (0.0, 0.0)
         filtered_speed = raw_speed if raw_speed >= 0.04 else 0.0
-
         raw_yaw_rate = abs(angular_vel[2])
-        filtered_yaw_rate = angular_vel[2] if raw_yaw_rate >= 0.05 else 0.0
+        filtered_yaw_rate = raw_yaw_rate if raw_yaw_rate >= 0.05 else 0.0
 
+        # --- 1. FIXED JITTER PENALTY ---
+        # Scaled way down (0.005) and capped so it can never dominate the reward
+        action_accel = action - 2.0 * self.last_action + self.last_last_action
+        raw_jitter = float(np.mean(np.square(action_accel)))
+        jitter_penalty = -0.005 * min(raw_jitter, 10.0)
+
+        # --- Base Components ---
         height_error = pos[2] - self.target_height
         r_height = math.exp(-150.0 * (height_error ** 2))
         r_stability = math.exp(-25.0 * (roll**2 + pitch**2))
@@ -302,12 +324,20 @@ class JackBotEnv(gym.Env):
         r_smoothness = math.exp(-0.1 * np.mean(np.square(action - previous_action)))
 
         r_lin_vel, r_ang_vel, stillness_penalty = 0.0, 0.0, 0.0
+        stand_penalty = 0.0
 
+        # --- 2. COMMAND IS ZERO: STANDING MODE ---
         if cmd_norm < 0.05 and abs(cmd_yaw) < 0.05:
             w_height, w_stability, w_pose, w_smoothness = 0.35, 0.35, 0.20, 0.10
-            total_reward = (w_height * r_height) + (w_stability * r_stability) + (w_pose * r_pose) + (w_smoothness * r_smoothness)
+            base_reward = (w_height * r_height) + (w_stability * r_stability) + (w_pose * r_pose) + (w_smoothness * r_smoothness)
+
+            # Soft quadratic penalty on filtered speed (gives a forgiving gap near 0)
+            stand_penalty = -0.5 * filtered_speed - 0.1 * filtered_yaw_rate
+            total_reward = base_reward + stand_penalty
+
+        # --- 3. COMMAND IS NON-ZERO: WALKING MODE ---
         else:
-            is_moving = (filtered_speed > 0.0) or (abs(filtered_yaw_rate) > 0.0)
+            is_moving = (filtered_speed > 0.0) or (filtered_yaw_rate > 0.0)
             target_vx, target_vy = cmd_vx * self.max_robot_speed, cmd_vy * self.max_robot_speed
             target_speed = math.hypot(target_vx, target_vy)
 
@@ -320,7 +350,7 @@ class JackBotEnv(gym.Env):
 
                 if target_speed > 0.08 and raw_speed < 0.03:
                     r_lin_vel = 0.0
-                    stillness_penalty = -0.25
+                    stillness_penalty = -0.1  # Softened from -0.25
 
                 w_lin_vel, w_ang_vel, w_height, w_stability, w_smoothness = 0.55, 0.15, 0.10, 0.12, 0.08
                 total_reward = (
@@ -328,7 +358,9 @@ class JackBotEnv(gym.Env):
                     + (w_stability * r_stability) + (w_smoothness * r_smoothness) + stillness_penalty
                 )
 
-        final_reward = float(total_reward / 10.0)
+        step_reward = float(total_reward / 10.0)
+        alive_bonus = 0.01
+        final_reward = max(0.0, step_reward + jitter_penalty + alive_bonus)
 
         self.last_reward_components = {
             "height": float(r_height),
@@ -337,13 +369,15 @@ class JackBotEnv(gym.Env):
             "smoothness": float(r_smoothness),
             "lin_vel": float(r_lin_vel),
             "ang_vel": float(r_ang_vel),
+            "jitter_penalty": float(jitter_penalty),
+            "stand_penalty": float(stand_penalty),
             "total": final_reward,
         }
         for k, v in self.last_reward_components.items():
             self.episode_reward_components_sum[k] += v
 
         return final_reward
-
+    
     def get_reward_component_averages(self) -> Dict[str, float]:
         steps = max(1, self.step_count)
         return {k: v / steps for k, v in self.episode_reward_components_sum.items()}
@@ -377,7 +411,7 @@ class JackBotEnv(gym.Env):
         )
 
     def _update_robot_failure(self):
-        if self.is_failed or self.step_count < 15:
+        if self.is_failed:
             return
 
         position, (roll, pitch, _) = self.sim.get_robot_pose_and_rpy()

ml/run_eval_training.py

@@ -4,6 +4,9 @@ ml/run_eval_training.py - Benchmark reward system across all curriculum phases.
 import time
 import numpy as np
 from ml.env import JackBotEnv, CurriculumPhase
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
 
 def evaluate_kinematics(episode_length: int = 1000):
     env = JackBotEnv(

ml/run_train.py

@@ -110,7 +110,7 @@ def main():
         eval_env,
         best_model_save_path=best_model_path,
         log_path="ml/logs/results",
-        eval_freq=max(1, 10_000 // args.num_workers),
+        eval_freq=max(1, 20_000 // args.num_workers),
         deterministic=True,
         render=False,
     )

simulation.py

@@ -82,7 +82,7 @@ class Simulation:
                 jointIndex=joint_index,
                 controlMode=p.POSITION_CONTROL,
                 targetPosition=float(target_angle),
-                force=500,
+                force=30,
                 physicsClientId=self.physics_client
             )
 
@@ -116,7 +116,7 @@ class Simulation:
 
         p.resetBasePositionAndOrientation(self.robot_id, pos, orn, physicsClientId=self.physics_client)
         p.resetBaseVelocity(self.robot_id, linearVelocity=lin_v, angularVelocity=ang_v, physicsClientId=self.physics_client)
-
+    
     # --- TELEMETRY (GETTERS) ---
 
     def get_robot_pose(self) -> Tuple[List[float], List[float]]:
@@ -141,6 +141,23 @@ class Simulation:
         joint_states = p.getJointStates(self.robot_id, self.revolute_joints, physicsClientId=self.physics_client)
         return np.array([state[0] for state in joint_states], dtype=np.float32)
 
+    def _get_urdf_joint_limits(self) -> Tuple[np.ndarray, np.ndarray]:
+        """Dynamically reads lower and upper limits for all revolute joints from PyBullet."""
+        lower_limits = []
+        upper_limits = []
+        
+        # Iterate through joints in PyBullet
+        for j_idx in range(p.getNumJoints(self.robot_id, physicsClientId=self.physics_client)):
+            info = p.getJointInfo(self.robot_id, j_idx, physicsClientId=self.physics_client)
+            joint_type = info[2]
+            
+            # Only collect limits for revolute joints
+            if joint_type == p.JOINT_REVOLUTE:
+                lower_limits.append(info[8])  # Index 8 = jointLowerLimit
+                upper_limits.append(info[9])  # Index 9 = jointUpperLimit
+                
+        return np.array(lower_limits, dtype=np.float32), np.array(upper_limits, dtype=np.float32)
+
     # --- SIMULATION LIFECYCLE CONTROLS ---
 
     def step(self) -> None:
@@ -153,8 +170,13 @@ class Simulation:
         for j in range(num_joints):
             p.changeVisualShape(self.robot_id, j, rgbaColor=rgba, physicsClientId=self.physics_client)
 
-    def settle_and_measure_height(self, steps: int = 200, fallback_height: float = 0.122) -> float:
+    def settle_and_measure_height(
+        self, target_angles: Optional[np.ndarray] = None, steps: int = 100, fallback_height: float = 0.122
+    ) -> float:
+        """Settles the robot into the ground while actively holding target joint angles."""
         for _ in range(steps):
+            if target_angles is not None:
+                self.set_robot_joint_angles(target_angles)
             self.step()
         pos, _ = self.get_robot_pose()
         return pos[2] if pos[2] > 0.0 else fallback_height