DC-Ada：仅奖励的去中心化传感器适应用于异构多机器人团队
DC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams
AI总结：
DC-Ada通过冻结预训练共享策略并适应紧凑的机器人观测转换，解决异构多机器人团队中传感器差异导致的性能下降问题，展示了在不同任务和指标下的有效性。
摘要：
异构性是部署多机器人团队的核心特征：平台在感知模态、范围、视野和故障模式上往往存在差异。在部署到缺少或不匹配传感器的机器人上时，即使任务和动作接口不变，训练于名义感知条件下的控制器性能会急剧下降。本文提出DC-Ada，一种仅基于奖励的去中心化适应方法，通过冻结预训练的共享策略并适应紧凑的机器人观测转换，将异构感知映射到固定的推理接口。DC-Ada是梯度自由且通信最小化的：它使用预算化的接受/拒绝随机搜索，在严格步骤预算下使用短共同随机数滚动生成。在确定性的2D多机器人模拟器中，针对仓库物流、搜索救援和协作映射任务，评估DC-Ada在四个异构性范围（H0-H3）和五个种子下的性能，预算为每个运行200,000个联合环境步骤。结果表明，异构性会显著降低冻结的共享策略性能，且没有单一的缓解方法在所有任务和指标上占优。观测归一化在仓库物流中对奖励鲁棒性最强，在搜索救援中具有竞争力，而冻结的共享策略在协作映射中对奖励效果最佳。DC-Ada提供了一个有用的互补操作点：它在严重的覆盖基于映射中显著提高完成度，仅需标量团队回报，无需策略微调或持久通信。这些结果使DC-Ada成为异构团队部署时的实用适应方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
