Add advanced robotics AGI feature modules

advanced/__init__.py

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+"""Advanced robotics AGI system."""

advanced/collaboration/__init__.py

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+"""Human-robot collaboration module for robotics AGI."""
+from .intent_prediction import IntentPredictor
+from .proactive_assistance import ProactiveAssistant
+from .handover import HandoverController
+from .shared_autonomy import SharedAutonomy
+
+__all__ = ["IntentPredictor", "ProactiveAssistant", "HandoverController", "SharedAutonomy"]

advanced/collaboration/handover.py

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+"""Handover controller for smooth object exchanges between robot and human."""
+import logging
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class HandoverController:
+    """Plans and executes smooth robot-human object handovers."""
+
+    def __init__(self) -> None:
+        """Initialize HandoverController."""
+        self.handover_history: list[dict] = []
+        self.approach_velocity = 0.1
+
+    def predict_handover(self, human_state: dict) -> dict:
+        """Predict when and where a handover will be requested.
+
+        Args:
+            human_state: Dict with human pose, gaze, and hand positions.
+
+        Returns:
+            Dict with predicted handover location and timing.
+        """
+        logger.info("Predicting handover from human state")
+        hand_pos = human_state.get("right_hand_position", [0.5, 0.0, 1.0])
+        gaze_target = human_state.get("gaze_target", "object")
+        handover_likely = random.random() > 0.3
+        return {
+            "status": "predicted",
+            "handover_likely": handover_likely,
+            "predicted_location": hand_pos,
+            "gaze_target": gaze_target,
+            "estimated_time_s": random.uniform(0.5, 3.0),
+            "confidence": random.uniform(0.6, 0.95),
+        }
+
+    def execute_handover(self, object_desc: dict, target: dict) -> dict:
+        """Execute a smooth object handover to or from a human.
+
+        Args:
+            object_desc: Dict with object being handed over.
+            target: Dict with target (human) position and handover mode.
+
+        Returns:
+            Dict with handover execution result and force profile.
+        """
+        logger.info("Executing handover of '%s' to '%s'", object_desc.get("name", "object"), target.get("id", "human"))
+        success = random.random() > 0.1
+        force_profile = [random.uniform(0.5, 5.0) for _ in range(5)]
+        record = {"object": object_desc.get("name"), "target": target.get("id"), "success": success}
+        self.handover_history.append(record)
+        return {
+            "status": "success" if success else "failed",
+            "object": object_desc.get("name", "object"),
+            "target": target.get("id", "human"),
+            "handover_mode": target.get("mode", "give"),
+            "force_profile_N": force_profile,
+            "handover_time_s": random.uniform(1.0, 4.0),
+            "load_transfer_complete": success,
+        }

advanced/collaboration/intent_prediction.py

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+"""Intent prediction for human-robot collaboration."""
+import logging
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class IntentPredictor:
+    """Predicts human intent and next actions for proactive collaboration."""
+
+    def __init__(self) -> None:
+        """Initialize IntentPredictor."""
+        self.action_vocabulary = ["pick_up", "put_down", "open", "close", "hand_over", "point", "walk_to"]
+
+    def predict_next_action(self, human_trajectory: list) -> dict:
+        """Predict the next action a human will take given their trajectory.
+
+        Args:
+            human_trajectory: List of position/state dicts over time.
+
+        Returns:
+            Dict with predicted next action and probability distribution.
+        """
+        logger.info("Predicting next action from %d-step trajectory", len(human_trajectory))
+        action_probs = {a: random.uniform(0.05, 1.0) for a in self.action_vocabulary}
+        total = sum(action_probs.values())
+        action_probs = {a: p / total for a, p in action_probs.items()}
+        top_action = max(action_probs, key=lambda k: action_probs[k])
+        return {
+            "status": "predicted",
+            "predicted_action": top_action,
+            "action_probabilities": action_probs,
+            "confidence": action_probs[top_action],
+            "trajectory_length": len(human_trajectory),
+        }
+
+    def infer_goal(self, partial_actions: list) -> dict:
+        """Infer the human's collaboration goal from partial action observations.
+
+        Args:
+            partial_actions: List of actions observed so far.
+
+        Returns:
+            Dict with inferred goal and estimated completion.
+        """
+        logger.info("Inferring collaboration goal from %d actions", len(partial_actions))
+        possible_goals = ["joint_assembly", "handing_object", "collaborative_carry", "teaching_task"]
+        inferred = random.choice(possible_goals)
+        completion = min(len(partial_actions) / 5.0 * 100, 100)
+        return {
+            "status": "inferred",
+            "inferred_goal": inferred,
+            "goal_completion_pct": completion,
+            "partial_actions": partial_actions,
+            "confidence": random.uniform(0.55, 0.92),
+        }

advanced/collaboration/proactive_assistance.py

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+"""Proactive assistance for anticipating and meeting human needs."""
+import logging
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class ProactiveAssistant:
+    """Anticipates human needs and prepares assistance proactively."""
+
+    def __init__(self) -> None:
+        """Initialize ProactiveAssistant."""
+        self.assistance_history: list[dict] = []
+        self.anticipation_horizon_s = 5.0
+
+    def anticipate_needs(self, human_activity: dict) -> dict:
+        """Anticipate what the human will need based on current activity.
+
+        Args:
+            human_activity: Dict with current human activity and context.
+
+        Returns:
+            Dict with predicted needs and preparation priorities.
+        """
+        logger.info("Anticipating needs for activity: %s", human_activity.get("type", "unknown"))
+        activity = human_activity.get("type", "working")
+        needs_map = {
+            "cooking": ["fetch_ingredient", "hold_bowl", "hand_utensil"],
+            "assembly": ["hand_tool", "hold_part", "inspect_joint"],
+            "working": ["fetch_document", "hold_item", "provide_support"],
+        }
+        predicted_needs = needs_map.get(activity, ["provide_support", "fetch_item"])
+        priorities = {need: random.uniform(0.3, 1.0) for need in predicted_needs}
+        return {
+            "status": "anticipated",
+            "activity": activity,
+            "predicted_needs": predicted_needs,
+            "need_priorities": priorities,
+            "top_need": max(priorities, key=lambda k: priorities[k]),
+            "anticipation_confidence": random.uniform(0.5, 0.9),
+        }
+
+    def prepare_assistance(self, predicted_need: dict) -> dict:
+        """Prepare the robot to provide the predicted assistance.
+
+        Args:
+            predicted_need: Dict with need type and priority.
+
+        Returns:
+            Dict with preparation steps and readiness status.
+        """
+        logger.info("Preparing assistance for: %s", predicted_need.get("type", "?"))
+        need_type = predicted_need.get("type", "support")
+        preparation_steps = {
+            "fetch_item": ["navigate_to_item", "grasp_item", "navigate_to_human"],
+            "hold_part": ["position_arm", "open_gripper", "wait_for_item"],
+            "support": ["move_to_position", "adopt_ready_pose"],
+        }
+        steps = preparation_steps.get(need_type, ["position_for_assistance"])
+        record = {"need": need_type, "steps": steps, "ready": True}
+        self.assistance_history.append(record)
+        return {
+            "status": "prepared",
+            "need_type": need_type,
+            "preparation_steps": steps,
+            "ready": True,
+            "preparation_time_s": random.uniform(1.0, 5.0),
+        }

advanced/collaboration/shared_autonomy.py

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+"""Shared autonomy for blending human and robot control."""
+import logging
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class SharedAutonomy:
+    """Blends human teleoperation input with autonomous robot assistance."""
+
+    def __init__(self) -> None:
+        """Initialize SharedAutonomy."""
+        self.autonomy_level: float = 0.5
+        self.blend_history: list[dict] = []
+
+    def blend_control(self, human_input: dict, robot_policy: dict) -> dict:
+        """Blend human teleoperation input with autonomous robot policy.
+
+        Args:
+            human_input: Dict with human control commands and intent.
+            robot_policy: Dict with autonomous policy output.
+
+        Returns:
+            Dict with blended control command and contribution breakdown.
+        """
+        logger.info("Blending control: autonomy_level=%.2f", self.autonomy_level)
+        h_cmd = human_input.get("command", [0.0, 0.0, 0.0])
+        r_cmd = robot_policy.get("command", [0.0, 0.0, 0.0])
+        if isinstance(h_cmd, list) and isinstance(r_cmd, list):
+            n = min(len(h_cmd), len(r_cmd))
+            blended = [(1 - self.autonomy_level) * h_cmd[i] + self.autonomy_level * r_cmd[i] for i in range(n)]
+        else:
+            blended = [0.0, 0.0, 0.0]
+        record = {"autonomy_level": self.autonomy_level, "blended": blended}
+        self.blend_history.append(record)
+        return {
+            "status": "blended",
+            "blended_command": blended,
+            "human_contribution": 1.0 - self.autonomy_level,
+            "robot_contribution": self.autonomy_level,
+            "autonomy_level": self.autonomy_level,
+            "blend_mode": "linear",
+        }
+
+    def adjust_autonomy_level(self, performance: dict) -> dict:
+        """Dynamically adjust the autonomy level based on task performance.
+
+        Args:
+            performance: Dict with performance metrics and error rates.
+
+        Returns:
+            Dict with new autonomy level and adjustment rationale.
+        """
+        logger.info("Adjusting autonomy level based on performance")
+        human_error_rate = performance.get("human_error_rate", random.uniform(0.0, 0.3))
+        task_difficulty = performance.get("task_difficulty", random.uniform(0.2, 0.8))
+        old_level = self.autonomy_level
+        if human_error_rate > 0.2:
+            self.autonomy_level = min(1.0, self.autonomy_level + 0.1)
+        elif human_error_rate < 0.05:
+            self.autonomy_level = max(0.0, self.autonomy_level - 0.1)
+        return {
+            "status": "adjusted",
+            "old_autonomy_level": old_level,
+            "new_autonomy_level": self.autonomy_level,
+            "adjustment": self.autonomy_level - old_level,
+            "human_error_rate": human_error_rate,
+            "task_difficulty": task_difficulty,
+        }

advanced/diagnosis/__init__.py

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+"""Diagnosis and self-repair module for robotics AGI."""
+from .self_diagnostics import SelfDiagnostics
+from .self_repair import SelfRepair
+from .anomaly_detection import AnomalyDetector
+from .predictive_maintenance import PredictiveMaintenance
+
+__all__ = ["SelfDiagnostics", "SelfRepair", "AnomalyDetector", "PredictiveMaintenance"]

advanced/diagnosis/anomaly_detection.py

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+"""Anomaly detection using statistical methods."""
+import logging
+import math
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class AnomalyDetector:
+    """Statistical anomaly detector using mean and standard deviation bounds."""
+
+    def __init__(self, z_threshold: float = 3.0) -> None:
+        """Initialize AnomalyDetector."""
+        self.z_threshold = z_threshold
+        self.mean: float | None = None
+        self.std: float | None = None
+        self.is_fitted = False
+
+    def fit(self, normal_data: list) -> dict:
+        """Fit the anomaly detector on normal (non-anomalous) data.
+
+        Args:
+            normal_data: List of numeric values representing normal operation.
+
+        Returns:
+            Dict with fitted statistics and model info.
+        """
+        logger.info("Fitting anomaly detector on %d samples", len(normal_data))
+        if not normal_data:
+            return {"status": "error", "message": "Empty dataset"}
+        numeric = [float(x) if isinstance(x, (int, float)) else random.uniform(0, 1) for x in normal_data]
+        self.mean = sum(numeric) / len(numeric)
+        variance = sum((x - self.mean) ** 2 for x in numeric) / len(numeric)
+        self.std = math.sqrt(variance) if variance > 0 else 1e-9
+        self.is_fitted = True
+        return {
+            "status": "fitted",
+            "mean": self.mean,
+            "std": self.std,
+            "num_samples": len(normal_data),
+            "z_threshold": self.z_threshold,
+        }
+
+    def detect(self, data: list) -> dict:
+        """Detect anomalies in new data using the fitted model.
+
+        Args:
+            data: List of numeric values to check for anomalies.
+
+        Returns:
+            Dict with anomaly flags, z-scores, and summary.
+        """
+        logger.info("Detecting anomalies in %d samples", len(data))
+        if not self.is_fitted:
+            return {"status": "error", "message": "Detector not fitted", "anomalies": []}
+        results = []
+        for i, x in enumerate(data):
+            val = float(x) if isinstance(x, (int, float)) else random.uniform(0, 1)
+            z_score = abs(val - self.mean) / max(self.std, 1e-9)
+            is_anomaly = z_score > self.z_threshold
+            results.append({"index": i, "value": val, "z_score": z_score, "is_anomaly": is_anomaly})
+        anomalies = [r for r in results if r["is_anomaly"]]
+        return {
+            "status": "detected",
+            "total_samples": len(data),
+            "anomalies_found": len(anomalies),
+            "anomaly_rate": len(anomalies) / max(len(data), 1),
+            "anomalies": anomalies,
+            "results": results,
+        }