Optimizing Autonomous Decision-Making in Robots through Meta-Learning Algorithms
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Abstract
Autonomous robotic systems must efficiently adapt to dynamic environments and generalize across tasks to be effective in real-world applications. However, traditional reinforcement learning (RL) models face significant challenges, including the need for extensive retraining, reliance on large task-specific datasets, and slow adaptation times. To address these issues, this paper proposes a novel meta-learning-based framework that leverages Long Short-Term Memory (LSTM) networks and LiDAR sensor data to enable robots to generalize across tasks and quickly adapt to new environments with minimal retraining. Despite challenges such as handling high-dimensional sensory inputs like LiDAR and the computational complexity of LSTM networks, the framework integrates few-shot learning to overcome data limitations and employs a dual reward function to balance task-specific performance with long-term generalization. The proposed system achieves a task success rate of 93.2%, significantly outperforming traditional RL models (85.7%) and static task-specific models (78.4%). Additionally, it demonstrates superior path efficiency, with an average of 85.2%, compared to 78.9% for RL models. The system adapts to new tasks in an average of 9.5 seconds, far faster than RL approaches, which require 23.7 seconds on average. Moreover, the meta-learning model requires smaller parameter updates (Δθ = 0.012) than traditional RL (Δθ = 0.021), indicating more efficient learning. Despite the challenges of handling complex sensor data and ensuring scalability, these results demonstrate the potential of meta-learning to significantly improve task generalization, adaptability, and computational efficiency in autonomous robotic systems. Future work will explore solutions to further optimize scalability and enhance the system's robustness in more complex environments.
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