Scale Robot Policy Evaluation with Ray

The blog post discusses the challenges and solutions involved in evaluating robot foundation models using Ray and Anyscale. It highlights the necessity of policy evaluation in robotics, where real-robot testing can be slow, costly, and unsafe, making simulation a vital alternative due to its safety, repeatability, and cost-effectiveness. However, simulation introduces a distributed-systems problem, requiring the coordination of GPU-heavy simulation and policy inference across numerous rollouts. Ray and Anyscale address three main challenges: ensuring closed-loop execution within simulation, separating GPU workloads to prevent resource contention, and efficiently sharing policy replicas across multiple rollouts. By disaggregating simulators and policy inference into separate processes and scaling them independently with Ray Serve, the solution enables parallel evaluation at scale, transforming evaluation into a scalable service akin to modern AI workloads. This approach allows for seamless scaling across GPU clusters, leveraging Ray’s primitives for distributed inference and offering a reproducible, flexible infrastructure for evaluating robotics models.

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