Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems

In mobile robotics, perception in uncontrolled environments like autonomous driving is a central hurdle. Existing active learning frameworks can help enhance perception by efficiently selecting data samples for labeling, but they are often constrained by the necessity of full data availability in data centers, hindering real-time, on-field adaptations. To address this, our work unveils a novel active learning formulation optimized for multi-robot settings. It harnesses the collaborative power of several robotic agents, considerably enhancing the data acquisition and synchronization processes. Experimental evidence indicates that our approach markedly surpasses traditional active learning frameworks by up to 2.5 percent points and 90% less data uploads, delivering new possibilities for advancements in the realms of mobile robotics and autonomous systems.