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Design a physical AI system from the job it must perform, the environment in which it must operate, and the consequences of failure. Those requirements determine what the machine must sense, which software functions need learned models, how much compute can run within the latency and power envelope, and what actuators and control loops can produce the required motion. A camera, a foundation model, or a powerful processor is not a complete robot architecture.
The practical workflow is sequential but iterative: specify the task, make the required state observable, assign software responsibilities, size compute against measured workloads, engineer the actuator and control chain, then validate in simulation and on physical hardware. The sections below turn that workflow into design decisions you can document and test.
What a physical AI system actually contains
A physical AI system is a closed loop. Sensors observe the robot and its surroundings; software converts those observations into estimates and decisions; controllers turn decisions into commands; actuators change the physical state; and monitoring detects conditions that require a slowdown, stop, or safe-state transition.
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One useful block diagram is:
Environment and task → sensors → synchronization and preprocessing → perception and state estimation → planning or policy inference → feedback control → actuator interfaces → mechanical system → environment.
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Safety monitoring, diagnostics, logging, and communications cut across every block. A learned model can implement one part of perception, planning, or policy inference, but it does not remove the need for state estimation, interfaces, deterministic control, timing analysis, or protective behavior.
In a ROS 2-based implementation, nodes exchange messages, services, or actions over defined interfaces. A robot description such as URDF records links, joints, meshes, and related structure so that simulators and applications share a consistent kinematic model. NVIDIA describes Isaac ROS as “an agent-friendly, open source software foundation for building AI-powered robots with ROS 2”; that is NVIDIA’s product description, not an independent endorsement. NVIDIA Isaac ROS documentation illustrates the modular approach, but the same system boundaries can be implemented with other software.
1. Specify the job and operating envelope before choosing hardware
Start with a written task specification rather than a preferred model or board. State what success looks like, where the machine operates, who or what shares the space, and what happens when it is wrong. This document becomes the source of sensor requirements, timing budgets, actuator requirements, and validation cases.
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- Map surfaces, obstacles, object sizes, reflectivity, transparency, lighting, dust, weather, vibration, and temperature.
- List people, vehicles, animals, tools, and other machines that can move unpredictably.
- Record whether the robot is fixed, wheeled, legged, aerial, underwater, or attached to another mechanism.
- Define communications availability and whether operation must continue when a network or remote service is unavailable.
Turn the task into measurable outcomes
- Define completion criteria: for example, placing an item in a tolerance zone, following a route, or maintaining a pose while applying contact force.
- Set allowable position, orientation, classification, timing, and force errors.
- Specify duty cycle: occasional demonstrations, continuous shifts, or repeated high-load operation.
- Document failure consequences and the required response, such as controlled stop, retraction, braking, or operator takeover.
Derive engineering budgets
For each behavior, write the observable quantities, update rate, maximum end-to-end latency, available energy, thermal limits, mass, and space. Include startup time, degraded modes, maintenance access, and data-retention requirements. These values are project requirements, not universal figures; they must come from the task and risk analysis.
2. Choose sensors by observability, not by a fashionable sensor list
Ask which hidden state the robot must estimate. Geometry and obstacles, object identity and pose, robot motion, contact, force, and localization each require different evidence. No single sensor sees every state reliably, and a reference architecture that lists many sensor classes does not mean every robot needs all of them.
| Sensor class | Useful observations | Questions to resolve before selection |
|---|---|---|
| RGB camera | Color, texture, visual features, and object appearance | How do lighting changes, glare, motion blur, and occlusion affect detection? |
| RGB-D camera | Image plus per-pixel depth for local geometry and object pose | What depth range, accuracy, frame rate, and lighting tolerance are required? |
| 2D or 3D lidar | Range scans or point clouds for obstacles, geometry, and mapping | Are surfaces reflective, transparent, dusty, or outside the useful range? |
| IMU | Acceleration and angular velocity for motion estimation | How will bias, vibration, drift, and time synchronization be handled? |
| Contact or force sensor | Touch, load, collision, and interaction force | Where must contact be detected, and what force range and response time are safe? |
| Radar | Range and motion cues in conditions where optical sensing may degrade | What resolution, interference tolerance, and target separation are needed? |
| Ultrasonic sensor | Short-range proximity or distance measurements | Are surfaces and angles suitable for reliable echoes? |
These classes appear in NVIDIA Isaac Sim’s ROS 2 reference architecture, which also names Intel RealSense among common camera examples. That establishes a category and an example, not a recommendation, price, current availability, or test result for a particular camera.
Design the mounting and timing
Field of view, blind spots, range, mounting height, vibration isolation, cable routing, and protection from dust or impact can matter as much as the sensing principle. Define a common time base and record timestamps at acquisition. Estimate the effect of transport and processing delay on control. If sensors are mounted on moving joints, account for their changing pose in the robot model.
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Plan calibration and sensor failure detection
Specify intrinsic calibration, camera-to-camera or sensor-to-body transforms, and how calibration will be checked after maintenance. Add health checks for stale timestamps, frozen images, impossible ranges, excessive noise, and disagreement between complementary sensors. Decide what the robot does when a sensor is unavailable: continue with reduced capability, slow down, stop, or request help.
Use complementary evidence deliberately
For example, an RGB-D camera can provide local object geometry, an IMU can stabilize short-term motion estimates, and lidar can support wider-area obstacle detection. The combination should be justified by an observability gap in the task specification, not by a desire to include every available sensor.
3. Define the software and model boundary
Write down every function between raw sensor data and motor commands. A typical decomposition is:
- Drivers and synchronization: acquire data, timestamp it, and expose diagnostics.
- Preprocessing: rectify images, filter or segment point clouds, remove invalid values, and normalize inputs.
- Perception: detect, classify, segment, or estimate object pose.
- State estimation: combine observations into robot pose, velocity, map, contact state, or other tracked variables.
- Task and motion planning: choose goals, paths, grasps, trajectories, or recovery actions.
- Low-level feedback control: regulate position, velocity, torque, force, or impedance at a known update rate.
- Actuator interface: enforce command limits, convert units, and report measured state.
- Monitoring and safe state: supervise timing, limits, communications, collisions, and operator commands.
Assign learned models only where they add value
A neural network may detect an object, estimate pose, reconstruct a scene, predict motion, or produce a policy action. Document its input contract, output contract, confidence or uncertainty signal, execution time, and behavior outside its training distribution. Keep hard limits, watchdogs, collision checks, and emergency behavior outside an opaque model unless they are independently verified.
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Keep interfaces stable
Use explicit message schemas for units, coordinate frames, timestamps, and validity. A planner should not silently assume that a perception node uses a different frame convention or that a controller can accept commands at an unspecified rate. Version the robot description and configuration alongside software so that simulation and hardware use the same link and joint names.
Use ROS 2 or another middleware as an integration choice
ROS 2 can connect simulation and application components through standard interfaces, and Isaac ROS provides modular packages and accelerated workflows as one implementation example. The documentation is vendor material; it does not establish that those packages are mandatory or best for every robot. Evaluate middleware on determinism, tooling, language support, lifecycle management, security, and the interfaces your team must maintain.
4. Size compute for the real workload
Compute selection follows the measured workload and deployment constraints. List every pipeline, its input dimensions and rate, peak rather than average demand, memory use, data movement, and maximum allowed latency. Include preprocessing, model inference, localization, planning, logging, and safety monitoring; an accelerator sized only for neural-network inference can still miss the system deadline.
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| Deployment location | Strengths | Constraints to verify |
|---|---|---|
| Workstation or laboratory server | High development capacity, rapid iteration, and convenient visualization | It may not meet robot mass, power, thermal, vibration, or connectivity limits. |
| Embedded computer on the robot | Local decisions with predictable network independence and lower data movement | Thermal headroom, memory, storage, model optimization, and sustained performance require testing. |
| Remote or cloud processing | Can provide substantial resources for non-time-critical workloads or fleet analytics | Network delay, outages, privacy, bandwidth, and safe behavior during disconnection must be addressed. |
Build a latency budget
Break the path into sensor exposure, transfer, preprocessing, inference, planning, command transmission, actuator response, and mechanical settling. Measure worst-case and tail latency under concurrent load. A high average frame rate is not sufficient if occasional pauses exceed the time available for safe control.
Account for power and thermal behavior
Record continuous and peak power, cooling method, enclosure temperature, throttling behavior, battery impact, and startup recovery. Test the complete application rather than a model in isolation. If a workload is optional, define a degraded mode that preserves safe motion when compute is constrained.
Choose where each function runs
Time-critical feedback and protective checks generally belong on or close to the robot. Mapping, retraining, fleet analysis, and synthetic-data generation may run elsewhere when their delays are acceptable. The boundary should be explicit, with a defined local response to lost connectivity.
NVIDIA describes Isaac workflows across workstation and embedded Jetson deployment classes. That is an example of deployment range, not a universal board recommendation or comparative benchmark.
5. Match actuators, mechanics, and control as one system
Begin with the motion and interaction requirements: force or torque, speed, range of motion, precision, compliance, payload, inertia, duty cycle, and allowable backlash. Then select the actuator, transmission, drive electronics, position or force sensing, and control loop together. A motor that meets a nameplate torque at one operating point may not satisfy the complete thermal and dynamic duty cycle.
Specify the mechanical output
- For each joint or motion axis, define the required envelope of position, velocity, acceleration, force, and torque.
- Include payload, gravity, friction, impacts, cable forces, and external contact.
- State accuracy, repeatability, stiffness, compliance, and allowable overshoot.
- Document hard stops, travel limits, brakes, and the behavior after power loss.
Define the drive and feedback chain
Choose command modes deliberately: position, velocity, torque, force, or impedance. Specify encoder resolution, sampling rate, communication protocol, current limits, thermal protection, and fault reporting. The controller must know whether a command was accepted, saturated, delayed, or rejected. Add independent limits where a software error could create dangerous motion.
Keep the robot description consistent
Represent links, joints, collision geometry, inertial properties, coordinate frames, and actuator limits consistently in the software model. URDF is commonly used to describe robot structure; simulation and application components can then reference the same topology. NVIDIA’s learning materials also cover robot construction, differential controllers, and articulation systems, but they do not provide a universal actuator-sizing equation or a comparative motor specification.
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Design control in layers
A task planner can request a trajectory, a motion controller can generate joint targets, and a lower-level drive can regulate current or torque. Define update rates and authority at each layer. Verify that a stalled planner, delayed perception result, or dropped network packet cannot bypass lower-level limits or leave an actuator energized indefinitely.
6. Integrate in simulation, then prove behavior on hardware
Simulation reduces iteration cost and exposes interface errors before hardware is at risk. It does not prove that a robot is safe or that performance will transfer perfectly. Progress through increasingly realistic tests and record the differences between simulated and measured behavior.
- Build the digital model: import or define links, joints, inertias, collision geometry, actuator limits, and sensor poses.
- Configure virtual sensors: use the same topics, frames, units, timestamps, and failure signals expected by the application.
- Run software-in-the-loop (SIL): exercise perception, estimation, planning, and control against simulated dynamics and scripted scenarios.
- Generate and test synthetic data: vary lighting, object placement, viewpoints, and clutter, then check whether models remain useful on representative real data.
- Run hardware-in-the-loop (HIL): connect real controllers or electronics to simulated dynamics where appropriate, testing timing, command limits, and fault handling.
- Perform supervised hardware tests: begin with restrained motion, low speed, reduced payload, and clear exclusion zones. Expand the envelope only after measured behavior matches acceptance criteria.
- Close the simulation-to-reality gap: update masses, friction, sensor noise, latency, actuator response, and environmental assumptions from logged hardware data.
NVIDIA’s Isaac Sim learning material describes SIL, HIL, synthetic-data workflows, and validation in simulated and physical environments. A versioned overview also presents physically based simulation and testing. These workflows support development; they do not replace physical validation, risk assessment, or site-specific safety controls.
Make test cases traceable
Link every requirement to scenarios and pass/fail measurements. Include nominal operation, sensor dropout, stale data, blocked paths, unexpected objects, communication loss, actuator saturation, low battery, thermal throttling, and restart recovery. Keep logs of sensor inputs, model versions, commands, measured motion, faults, and operator interventions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare competing designs
When multiple architectures or components satisfy the basic task, compare them against the same requirements rather than selecting on a single benchmark.
| Comparison axis | What to measure or document |
|---|---|
| Task performance | Success criteria, error distribution, recovery rate, and performance across expected scenes |
| Observability | Coverage of geometry, identity, motion, contact, and robot state; blind spots and uncertainty |
| Timing | Worst-case end-to-end latency, jitter, synchronization quality, and control update rate |
| Compute | Throughput, memory, sustained load, startup time, and behavior under concurrent pipelines |
| Power and thermal | Continuous and peak draw, cooling requirements, throttling, battery impact, and enclosure limits |
| Actuation | Force or torque, speed, range, precision, compliance, payload, and fault response |
| Integration | Drivers, message interfaces, coordinate frames, tooling, diagnostics, and maintainability |
| Environment | Lighting, dust, moisture, vibration, temperature, electromagnetic interference, and network conditions |
| Validation effort | Calibration burden, simulation fidelity, test fixtures, data collection, and repeatability |
| Lifecycle | Supply continuity, update policy, repairability, cybersecurity maintenance, and documentation |
The cited documentation establishes several sensor classes and workstation-to-embedded deployment choices, but it does not provide head-to-head scores or a universal ranking. Gather independent measurements under your own operating conditions before making a final selection.
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The model works in a demo but fails in the site
Check lighting, viewpoints, object variation, motion blur, occlusion, sensor placement, and timing. Capture representative logs, quantify where confidence falls, and improve sensing, calibration, data, or task constraints before increasing model complexity.
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The robot reacts too late
Instrument timestamps through the entire pipeline. Separate exposure, transport, queueing, inference, planning, and actuator delay. Remove unnecessary copies, reduce input complexity, move critical functions locally, or lower the commanded speed until the measured latency fits the safety envelope.
Simulation and hardware disagree
Compare mass and inertia, friction, joint limits, actuator saturation, sensor noise, calibration transforms, and communication delay. Replay identical inputs where possible, then update the model from measured logs rather than tuning only by visual similarity.
Localization or mapping drifts
Inspect timestamp alignment, IMU bias, wheel or joint measurements, texture or geometry availability, and frame conventions. Add a complementary observation source only when it addresses the identified observability gap, and define behavior when localization confidence is low.
An actuator oscillates, saturates, or overheats
Check commanded profiles, controller gains, sampling jitter, transmission backlash, payload and inertia assumptions, current limits, cooling, and mechanical interference. Reduce the operating envelope and use a supervised test fixture while isolating the cause.
A sensor or network link disappears
Verify stale-data detection and watchdog behavior. The system should transition to the documented degraded or safe state, report the fault, and avoid issuing indefinite last-known commands.
An illustrative design walk-through
Consider a mobile manipulator that must pick a known class of packages from shelves in a mixed human workspace. The task specification would include shelf geometry, package variation, people and forklift traffic, allowable placement error, maximum approach speed, shift duration, and the response to a person entering the work zone.
Observability might require an RGB-D camera for local package geometry, lidar for wider obstacle coverage, an IMU for motion estimation, and contact or force sensing for grasp confirmation. The exact combination would be validated against shelf reflectivity, lighting, dust, and occlusion rather than assumed from a catalog.
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Compute testing would measure the complete navigation-and-manipulation pipeline on the intended embedded computer, including thermal behavior over a full shift. Simulation would cover shelf layouts, package poses, sensor dropouts, blocked routes, and controller saturation before supervised hardware trials. Logs from those trials would update sensor noise, latency, friction, and object-variation assumptions.
Quick Recap
Design checklist
- Task success, error tolerance, duty cycle, environment, and failure consequences are written down.
- Every required state has an identified observation source and an uncertainty or health signal.
- Sensor placement, calibration, synchronization, data rates, and dropout behavior are specified.
- Perception, estimation, planning, control, actuator interfaces, and safety monitoring have explicit boundaries.
- Model inputs, outputs, confidence, latency, and out-of-distribution behavior are documented.
- Compute is sized from measured end-to-end workload, memory, power, thermal, and connectivity constraints.
- Actuator force or torque, speed, range, precision, compliance, payload, duty cycle, and limits are verified.
- Robot links, joints, frames, inertias, collision geometry, and limits are consistent across software and simulation.
- SIL, synthetic-data, HIL, and progressive hardware tests cover nominal and failure scenarios.
- Simulation-to-reality mismatches are measured, logged, and fed back into models and requirements.
- Physical validation and a formal, site-appropriate safety process are complete before unrestricted operation.
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