Add server/tasks/track_linear_velocity.py

server/tasks/track_linear_velocity.py

@@ -0,0 +1,84 @@
+# Copyright (c) Space Robotics Lab, SnT, University of Luxembourg, SpaceR
+# RANS: arXiv:2310.07393
+
+"""
+TrackLinearVelocity Task
+========================
+The spacecraft must maintain a randomly sampled target linear velocity (vx_t, vy_t).
+
+Observation (6 values):
+    [Δvx, Δvy, cos(θ), sin(θ), vx, vy]
+    where Δv = v_target − v_current.
+
+Reward:
+    r = exp(-‖v_error‖² / (2·σ_v²))
+
+Episode terminates when ‖v_error‖ < tolerance.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any, Dict, Tuple
+
+import numpy as np
+
+from .base import BaseTask
+
+
+class TrackLinearVelocityTask(BaseTask):
+    """Track a target 2-D linear velocity in the world frame."""
+
+    _DEFAULTS: Dict[str, Any] = {
+        "tolerance": 0.05,           # success threshold (m/s)
+        "reward_sigma": 0.50,        # velocity reward width
+        "max_target_speed": 1.00,    # maximum sampled target speed (m/s)
+    }
+
+    def __init__(self, config: Dict[str, Any] | None = None) -> None:
+        super().__init__(config)
+        cfg = {**self._DEFAULTS, **(config or {})}
+        self.tolerance: float = cfg["tolerance"]
+        self.reward_sigma: float = cfg["reward_sigma"]
+        self.max_target_speed: float = cfg["max_target_speed"]
+
+        self._target_vel = np.zeros(2, dtype=np.float64)
+
+    # ------------------------------------------------------------------
+    # BaseTask interface
+    # ------------------------------------------------------------------
+
+    def reset(self, spacecraft_state: np.ndarray) -> Dict[str, Any]:
+        speed = np.random.uniform(0.0, self.max_target_speed)
+        direction = np.random.uniform(0.0, 2.0 * math.pi)
+        self._target_vel = np.array(
+            [speed * math.cos(direction), speed * math.sin(direction)]
+        )
+        return {"target_linear_velocity": self._target_vel.tolist()}
+
+    def get_observation(self, spacecraft_state: np.ndarray) -> np.ndarray:
+        _, _, theta, vx, vy, _ = spacecraft_state
+        dvx = self._target_vel[0] - vx
+        dvy = self._target_vel[1] - vy
+        return np.array(
+            [dvx, dvy, math.cos(theta), math.sin(theta), vx, vy],
+            dtype=np.float32,
+        )
+
+    def compute_reward(
+        self, spacecraft_state: np.ndarray
+    ) -> Tuple[float, bool, Dict[str, Any]]:
+        vx, vy = spacecraft_state[3], spacecraft_state[4]
+        vel_error = float(np.linalg.norm(self._target_vel - np.array([vx, vy])))
+        reward = self._reward_exponential(vel_error, self.reward_sigma)
+        goal_reached = vel_error < self.tolerance
+        info = {
+            "velocity_error_ms": vel_error,
+            "goal_reached": goal_reached,
+            "target_linear_velocity": self._target_vel.tolist(),
+        }
+        return reward, goal_reached, info
+
+    @property
+    def num_observations(self) -> int:
+        return 6