Autonomous navigation research has made remarkable technical progress, yet a critical gap remains: we optimize for success rates and path efficiency, but not for economic viability. CostNav addresses this by introducing the first navigation benchmark that evaluates robots through the lens of profitability—the metric that actually matters for commercial deployment.
CostNav models the complete economic lifecycle of delivery robots: upfront hardware and training costs, per-delivery expenses (energy, maintenance, crashes), and revenue generation constrained by real-world service-level agreements. By grounding evaluation in actual industry data—delivery pricing, energy rates, hardware costs—CostNav reveals that a robot with 80% success using cheap sensors might be more profitable than one with 95% success using expensive LiDAR.
Our framework enables systematic comparison of fundamentally different approaches: classical planning with expensive sensors vs. learning-based methods with cameras, on-device vs. cloud inference, and traditional training vs. cost-aware reinforcement learning. CostNav bridges the gap between impressive research demos and sustainable businesses, providing researchers and engineers with data-driven answers to deployment decisions that directly impact commercial viability.
Traditional navigation benchmarks celebrate 95% task completion rates and optimized path efficiency. But these metrics don't answer the questions that keep startup founders awake at night:
Academic benchmarks don't answer these questions. CostNav does.
CostNav models the entire economic lifecycle of a delivery robot:
Hardware costs, sensor investments, training expenses, data collection—all the upfront investments that need to be recovered.
Energy consumption from motors and sensors, battery degradation from charge cycles, maintenance costs from wear and tear, crash damage from collisions.
Not just "did it deliver?" but "did it deliver within the service-level agreement (SLA)?" In the real world, a delivery that takes 35 minutes when you promised 30 gets refunded. A delivery that arrives on time but spoils the food because of aggressive driving is refunded. Both timing and quality constraints define whether a delivery has truly created economic value.
All of this is grounded in real-world data: actual delivery service pricing, industry energy rates, hardware costs from commercial robots. This isn't theoretical—it's what real companies face every day.
Our initial release establishes a learning-based navigation baseline in realistic urban environments. But this is just the beginning.
CostNav lets you optimize for what actually matters in deployment. Explore cost-aware reward functions, evaluate trade-offs between sensor cost and performance, and publish work that directly translates to commercial value.
CostNav gives you data-driven answers to deployment decisions. No more guessing whether expensive sensors are worth it—you'll see the break-even analysis. No more wondering if cloud inference pays for itself—you'll see the profit margins.
CostNav bridges the gap between impressive demos and sustainable businesses. A robot that's technically impressive but economically unviable won't change the world. A robot that's profitable at scale will.
Imagine a world where navigation research papers include a "profitability" section alongside accuracy metrics. Where we optimize for dollars per delivery, not just success rates. Where choosing between navigation approaches is guided by break-even analysis, not just technical performance.
That's the world CostNav is building.
We're not saying traditional metrics don't matter—they absolutely do. But they're incomplete. A robot that's technically impressive but economically unviable won't change the world. A robot that's profitable at scale will.
We're releasing everything: the benchmark framework, cost models validated against industry data, simulation environment, evaluation code, and our baseline results. We want the community to build on this.
The autonomous navigation field has made incredible technical progress.
Now it's time to make it economically viable.
It's time to talk about money. It's time for CostNav.
CostNav will be presented at CES 2026. Technical report, benchmark, code, and models are available now with continual updates planned.
The current pre-release version includes our initial implementations for simulation, task design, training, evaluation, and—most importantly—metrics. We'll be rolling out continual improvements, so keep an eye on upcoming updates!
@article{seong2025costnav,
title={CostNav: A Navigation Benchmark for Cost-Aware Evaluation of Embodied Agents},
author={Seong, Haebin and Kim, Sungmin and Kim, Minchan and Cho, Yongjun and Joe, Myunchul and Choi, Suhwan and Jung, Jaeyoon and Youn, Jiyong and Kim, Yoonshik and Seong, Samwoo and others},
journal={arXiv preprint arXiv:2511.20216},
year={2025}
}