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：异构性是部署多机器人团队的一个显著特征：不同平台在感知模态、感知范围、视场和故障模式上往往存在差异。在标称感知条件下训练的控制器，当部署到传感器缺失或不匹配的机器人上时，即使任务和动作接口保持不变，其性能也会急剧下降。我们提出DC-Ada——一种仅使用奖励信号的分散式自适应方法，该方法冻结预训练的共享策略，转而自适应地调整每个机器人紧凑的观测变换，以将异构感知映射到固定的推理接口。DC-Ada无需梯度且通信需求极低：它在严格的步数预算下，利用带预算的接受/拒绝随机搜索，配合短共随机数轨迹展开。我们在一个确定性二维多机器人模拟器（涵盖仓库物流、搜索救援和协作建图）中，跨四种异构模式（H0–H3）以及五个随机种子（每个运行使用匹配的20万次联合环境步预算），将DC-Ada与四个基线方法进行了评估。结果表明，异构性会显著降低冻结共享策略的性能，且没有任何一种缓解方法在所有任务和指标中占优。观测归一化在仓库物流的奖励鲁棒性上表现最强，在搜索救援中具有竞争力；而冻结共享策略在协作建图的奖励上表现最强。DC-Ada提供了一个有用的互补操作点：在严重依赖覆盖率的建图任务中，它最显著地提升了完成度，同时仅需标量团队回报，无需策略微调或持续通信。这些结果使DC-Ada成为一种实用的异构团队部署时自适应方法。