Readily-available physical AI, with robotics assisting users in manufacturing, home, and warehouses scenarios, holds immense potential to improve safety, productivity, and assistance across a wide range of tasks. In many ways, AI for the physical world represents a major frontier for AI . Physical AI must operate in open, unpredictable environments, interact with both other robots and people, and work with a diversity of embodiments. Realizing this vision requires advances along three dimensions: robot hardware, embodied AI models, and systems infrastructure for training and inference. While robot hardware and the AI models have advanced rapidly in recent years, we turn our focus on a relatively under-addressed aspect: inference infrastructure of physical AI. Enabling robots to effectively and safely operate in the physical world will require sophisticated systems to handle large volumes of distributed inference compute.

Today, the prevailing approach to physical AI is to provision a GPU onboard the robot, e.g., by wiring a GPU to the robot. In this model, the robot’s inference will be confined to the onboard GPU, and provide the robot with the necessary chunks and sequence of actions for the execution of its tasks. While higher-level planning may be performed in the cloud, task execution typically remains tied to the robot itself. We challenge this assumption.  As physical AI models grow in size and sophistication, the constraints of onboard compute become increasingly apparent. GPUs consume significant power, reduce battery life, add cost and weight, and can limit the ability to run the latest generation of AI models. 

To better understand the systems implications of physical AI, we conducted the first systematic study of robotics workloads. We focused on mobile robotic manipulation, with the canonical task such as “check for rubbish in the kitchen and put it in the trash.” Such a task involves planning the path to the kitchen, perceiving the environment to find rubbish, navigating to the rubbish, picking up the rubbish, and navigating back to the trash can for disposal. We evaluated representative models across three core capabilities: semantic mapping and planning, navigation, and manipulation, as summarized in Figure 2. 

Offloading physical AI inference out of the robot improved its response time and accuracy, along with battery lifetime and cost. We evaluated the inference models across a range of onboard, edge, and cloud compute configurations. Details of the specific test hardware are available in our technical report. 

Benefits in task performance: Our evaluation shows offloading inference can significantly improve robot performance across mapping, planning, navigation, and manipulation workloads. Some smaller GPUs could not accommodate the mobile manipulation stack. On GPUs with sufficient memory, mapping and planning slowed by up to 383% compared to an A100, thus limiting the robot’s abilities in dynamic spaces. Navigation showed a 30% drop in its timely detection of obstacles with lighter GPUs. While the VLA models did not dramatically slow down with smaller GPUs, the slowdown was still sufficient to drop their accuracies by 50%. In other words, onboard GPUs limited the performance of the robots while offloading their inference to an on-premise or cloud GPU boosts their operations, as shown in the videos below and quantified in the graphs. As physical AI models continue to grow in size and complexity, the benefits of offloading are likely to become even more pronounced.

Readily-available physical AI, with robotics assisting users in manufacturing, home, and warehouses scenarios, holds immense potential to improve safety, productivity, and assistance across a wide range of tasks. In many ways, AI for the physical world represents a major frontier for AI . Physical AI must operate in open, unpredictable environments, interact with both other robots and people, and work with a diversity of embodiments. Realizing this vision requires advances along three dimensions: robot hardware, embodied AI models, and systems infrastructure for training and inference. While robot hardware and the AI models have advanced rapidly in recent years, we turn our focus on a relatively under-addressed aspect: inference infrastructure of physical AI. Enabling robots to effectively and safely operate in the physical world will require sophisticated systems to handle large volumes of distributed inference compute.

Today, the prevailing approach to physical AI is to provision a GPU onboard the robot, e.g., by wiring a GPU to the robot. In this model, the robot’s inference will be confined to the onboard GPU, and provide the robot with the necessary chunks and sequence of actions for the execution of its tasks. While higher-level planning may be performed in the cloud, task execution typically remains tied to the robot itself. We challenge this assumption.  As physical AI models grow in size and sophistication, the constraints of onboard compute become increasingly apparent. GPUs consume significant power, reduce battery life, add cost and weight, and can limit the ability to run the latest generation of AI models. 

To better understand the systems implications of physical AI, we conducted the first systematic study of robotics workloads. We focused on mobile robotic manipulation, with the canonical task such as “check for rubbish in the kitchen and put it in the trash.” Such a task involves planning the path to the kitchen, perceiving the environment to find rubbish, navigating to the rubbish, picking up the rubbish, and navigating back to the trash can for disposal. We evaluated representative models across three core capabilities: semantic mapping and planning, navigation, and manipulation, as summarized in Figure 2. 

Offloading physical AI inference out of the robot improved its response time and accuracy, along with battery lifetime and cost. We evaluated the inference models across a range of onboard, edge, and cloud compute configurations. Details of the specific test hardware are available in our technical report. 

Benefits in task performance: Our evaluation shows offloading inference can significantly improve robot performance across mapping, planning, navigation, and manipulation workloads. Some smaller GPUs could not accommodate the mobile manipulation stack. On GPUs with sufficient memory, mapping and planning slowed by up to 383% compared to an A100, thus limiting the robot’s abilities in dynamic spaces. Navigation showed a 30% drop in its timely detection of obstacles with lighter GPUs. While the VLA models did not dramatically slow down with smaller GPUs, the slowdown was still sufficient to drop their accuracies by 50%. In other words, onboard GPUs limited the performance of the robots while offloading their inference to an on-premise or cloud GPU boosts their operations, as shown in the videos below and quantified in the graphs. As physical AI models continue to grow in size and complexity, the benefits of offloading are likely to become even more pronounced.