Robots That LearnFrom Experience

from shodh_memory import Memory

# Initialize on robot controller (fully offline)
memory = Memory(storage_path="./robot_memory")

# Record observations and outcomes
memory.remember(
    "Approach angle 45deg caused gripper slip on part X",
    memory_type="Error",
    tags=["gripper", "part_x", "angle"]
)

memory.remember(
    "Approach angle 30deg successful for part X",
    memory_type="Learning",
    tags=["gripper", "part_x", "angle"]
)

# Before next action, recall relevant experience
results = memory.recall("gripper approach angle part X", limit=5)
for mem in results:
    print(f"{mem['content']} (type: {mem['experience_type']})")

# Hybrid retrieval - semantic + graph associations
results = memory.recall("gripper failures", mode="hybrid", limit=10)

# ROS2 integration example
import rclpy
from rclpy.node import Node

class RobotMemoryNode(Node):
    def __init__(self):
        super().__init__('memory_node')
        self.memory = Memory(storage_path="/opt/robot/memory")

    def action_callback(self, msg):
        # Record action outcomes
        if msg.success:
            self.memory.remember(
                f"Action {msg.action} succeeded with params {msg.params}",
                memory_type="Learning"
            )
        else:
            self.memory.remember(
                f"Action {msg.action} failed: {msg.error}",
                memory_type="Error"
            )

    def plan_action(self, task):
        # Recall similar past experiences before planning
        experiences = self.memory.recall(task, limit=5)
        return experiences