Multirobot collaboration offers significant potential for diverse applications, including tracking and surveillance. In this paper, we introduce an attention mechanism tailored for heterogeneous robot swarms characterized by varied sensing ranges. This mechanism effectively utilizes the swarm's intrinsic characteristics, enabling rapid information transmission and ensuring consistent collective responses to external stimuli. Additionally, we introduce a pigeon-inspired navigation strategy that effectively replaces the traditional obstacle repulsion term by preventing the swarm from becoming trapped in local min-ima and reducing oscillatory behaviors. To validate the efficacy of our algorithm, we have developed an autonomously designed PlusBot swarm platform, which consists of agile vibration-driven miniature robots. Each of them is equipped with its own computing and communication system and is capable of precise closed-loop motion control. This setup meets the requirements for conducting heterogeneous swarm movement experiments in indoor environments. Through comprehensive numerical simulations and real-world experiments, our method has demonstrated exceptional precision and adaptability in tracking dynamic targets. The comparative analysis under-scores the superiority of our approach, particularly in minimizing swarm collisions and ensuring safe navigation in dynamic target-tracking scenarios involving obstacles.

Learning a policy with great generalization to unseen environments remains challenging but critical in visual reinforcement learning. Despite the success of augmentation combination in the supervised learning generalization, naively applying it to visual RL algorithms may damage the training efficiency, suffering from serve performance degradation. In this paper, we first conduct qualitative analysis and illuminate the main causes: (i) high-variance gradient magnitudes and (ii) gradient conflicts existed in various augmentation methods. To alleviate these issues, we propose a general policy gradient optimization framework, named Conflict-aware Gradient Agreement Augmentation (CG2A), and better integrate augmentation combination into visual RL algorithms to address the generalization bias. In particular, CG2A develops a Gradient Agreement Solver to adaptively balance the varying gradient magnitudes, and introduces a Soft Gradient Surgery strategy to alleviate the gradient conflicts. Extensive experiments demonstrate that CG2A significantly improves the generalization performance and sample efficiency of visual RL algorithms.