Perception and Decision-Making Methods for Multi-Task Collaborative Robots Based on Embodied Intelligence
Keywords:
Embodied Intelligence, Multi-Agent Systems, Collaborative Robotics, Reinforcement Learning, Multi-Modal PerceptionAbstract
The paradigm of embodied intelligence emphasizes the indispensable role of physical interaction and morphology in the development of cognitive abilities for artificial agents. As the demand for scalable and flexible automation grows, multi-task collaborative robotics has emerged as a critical domain necessitating sophisticated perception and decision-making capabilities. This paper investigates advanced methodologies for enabling distributed robotic swarms and collaborative manipulators to perceive complex, dynamic environments and execute joint actions in multi-task scenarios. We propose a comprehensive framework that integrates multi-modal sensory fusion with decentralized reinforcement learning to address the high-dimensional state spaces and partial observability inherent in multi-agent physical systems. By treating perception not as a passive data collection process but as an active, task-driven mechanism, the proposed architecture drastically reduces the cognitive load on individual agents while maintaining robust collective performance. Furthermore, we introduce an embodied decision-making algorithm based on a multi-agent actor-critic architecture, formulated to optimize long-horizon collaborative tasks through intrinsic motivation and communication-efficient state sharing. Extensive simulations conducted in realistic physics environments demonstrate that our method significantly outperforms traditional centralized control and independent reinforcement learning baselines in terms of task success rate, collision avoidance, and adaptability to novel scenarios. The findings presented in this study provide a robust theoretical and empirical foundation for the next generation of embodied robotic systems operating in unconstrained, human-centric environments.
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