Offloaded inference for real-world physical AI robotics

Challenges a core assumption in robotics AI: Our research shows that running physical AI inference exclusively on onboard GPUs can limit robot performance, battery life, and scalability, and that offloading inference to edge or cloud GPUs can offer significant advantages. Demonstrates measurable benefits of inference offloading: Across representative mobile manipulation workloads, offloading improved task success rates, enabled larger AI models, and helped robots respond more effectively in dynamic, real-world environments. Extends robot operating time: Replacing power-hungry onboard AI compute with lightweight onboard hardware and remote inference can substantially improve battery life, enabling robots to operate longer between charges. Introduces a new capability in the Physical AI Toolchain: Developers can now containerize, deploy, and orchestrate robotics AI workloads across robots, edge infrastructure, and the cloud using Kubernetes-based…