Task Execution and Interactive Learning of Embodied Agents in Complex Open Environments

Abstract

The deployment of embodied artificial intelligence in complex, open-ended environments requires agents capable of robust task execution and continuous adaptation. Traditional reinforcement learning paradigms often falter when faced with infinite state spaces, novel object properties, and sparse reward signals inherent to unstructured real-world domains. This paper presents a comprehensive framework for task execution and interactive learning designed to enhance the sample efficiency and generalization capabilities of embodied agents. We formalize the interactive learning process within a Partially Observable Markov Decision Process and introduce a multimodal representation learning architecture that integrates environmental perception with active interaction mechanisms. By dynamically querying external knowledge sources or human supervisors when uncertainty thresholds are exceeded, the agent mitigates the risks of catastrophic failure and reduces the sample complexity of exploration. Our methodological contributions include an uncertainty-aware policy optimization algorithm and a memory-augmented state representation module that processes sequential observations to infer latent environmental dynamics. Extensive empirical evaluations conducted in procedurally generated three-dimensional simulators demonstrate that our proposed interactive learning approach significantly outperforms conventional deep reinforcement learning baselines in terms of task success rate, average episode length, and overall sample efficiency. The findings suggest that integrating active interaction modalities into the foundational learning loops of embodied agents is critical for bridging the gap between constrained simulated environments and dynamic open-world deployments.