Robotic systems, such as autonomous unmanned aerial vehicles (UAVs) and self-driving cars, have been widely deployed in many scenarios and have the potential to revolutionize the future generation of computing. To improve the performance and energy efficiency of robotic platforms, significant research efforts are being devoted to developing hardware accelerators for workloads that form bottlenecks in the robotics software pipeline. Although domain-specific accelerators can offer improved efficiency over general-purpose processors on isolated robotics benchmarks, system-level constraints such as data movement and contention over shared resources can significantly impact the achievable end-to-end acceleration. In addition, the closed-loop nature of robotic systems, where there is a tight interaction across different deployed environments, software stacks, and hardware architecture, further exacerbates the difficulties of evaluating robotics SoCs.