Heterogeneity is a defining feature of deployed multi-robot teams: platforms often differ in sensing modalities, ranges, fields of view, and failure patterns. Controllers trained under nominal sensing can degrade sharply when deployed on robots with missing or mismatched sensors, even when the task and action interface are unchanged. We present DC-Ada, a reward-only decentralized adaptation method that keeps a pretrained shared policy frozen and instead adapts compact per-robot observation transforms to map heterogeneous sensing into a fixed inference interface. DC-Ada is gradient-free and communication-minimal: it uses budgeted accept/reject random search with short common-random-number rollouts under a strict step budget. We evaluate DC-Ada against four baselines in a deterministic 2D multi-robot simulator covering warehouse logistics, search and rescue, and collaborative mapping, across four heterogeneity regimes (H0--H3) and five seeds with a matched budget of $200{,}000$ joint environment steps per run. Results show that heterogeneity can substantially degrade a frozen shared policy and that no single mitigation dominates across all tasks and metrics. Observation normalization is strongest for reward robustness in warehouse logistics and competitive in search and rescue, while the frozen shared policy is strongest for reward in collaborative mapping. DC-Ada offers a useful complementary operating point: it improves completion most clearly in severe coverage-based mapping while requiring only scalar team returns and no policy fine-tuning or persistent communication. These results position DC-Ada as a practical deploy-time adaptation method for heterogeneous teams.

翻译：异构性是实际部署的多机器人团队的显著特征：不同平台在传感模式、感知范围、视野范围及故障模式上常常存在差异。在标称传感条件下训练的控制策略，当部署在缺失或传感器不匹配的机器人上时，即使任务和动作接口保持不变，其性能也可能急剧下降。我们提出DC-Ada，一种仅依赖奖励的分散式适配方法，该方法保持预训练的共享策略参数固定，转而适配每个机器人独立的紧凑型观测变换，将异构传感映射到固定的推理接口。DC-Ada无需梯度且通信需求极小：它在严格的步骤预算下，利用预算约束的接受/拒绝随机搜索，配合短公共随机数回合进行优化。我们在确定性二维多机器人仿真器上，针对仓库物流、搜索救援及协作地图构建三类任务，跨越四种异构性等级（H0–H3），使用五个随机种子并以每次运行$200{,}000$步联合环境步的匹配预算，将DC-Ada与四种基线方法进行了比较。结果表明，异构性会显著降低冻结共享策略的性能，且没有单一缓解方法能在所有任务和指标上占据主导地位。在仓库物流任务中，观测归一化在奖励鲁棒性方面表现最强，并在搜索救援任务中具有竞争力；而冻结共享策略在协作地图构建任务中奖励表现最佳。DC-Ada提供了一个有用的互补操作点：在基于覆盖率的严重地图构建任务中，它最明显地提升了完成度，且仅需标量团队回报，无需策略微调或持续通信。这些结果使DC-Ada成为异构团队实际部署时的一种有效适配方法。