Solution — Physical AI
Robots that generalise beyond the taught point.
Learned policies for tasks that resist explicit programming — contact-rich assembly, deformable materials, high-mix handling — trained in simulation and transferred to real hardware.
Approach
Teach-and-repeat runs out where variation begins.
Classical robot programming is exact and, within its envelope, unbeatable. It fails when the part is flexible, the presentation varies, or the product mix changes faster than an engineer can re-teach. That boundary is where physical AI belongs — and recognising which side of it your task sits on is most of the engineering judgement.
We train in simulation with domain randomisation across lighting, friction, mass and geometry, then transfer to hardware with measured sim-to-real gap and a fallback to deterministic control. A learned policy that cannot be bounded and monitored does not go on a production line.
This is our most active research area. It is also the area where we most often advise a client that the conventional solution is the correct one — and we would rather tell you that in week one than in month six.
Scope
What is included.
Delivered as a defined scope with acceptance criteria, not as a time-and-materials estimate that drifts.
- 01Task feasibility assessmentlearned vs deterministic decision
- 02Simulation environment buildIsaac Lab, domain randomisation
- 03Imitation learningteleoperated demonstration capture
- 04Reinforcement learningreward shaping, curriculum
- 05Vision-language-action policiesinstruction-conditioned control
- 06World modelspredictive rollout for planning
- 07Sim-to-real transfermeasured gap, calibration
- 08Safety envelope designbounded authority, fallback control
- 09On-robot evaluationsuccess rate under real variation
- 10Continuous improvementdata flywheel from production
Applications
Where this is used.
Contact-rich assembly
Insertion, mating and alignment tasks where force feedback and adaptation beat a fixed path.
Deformable material handling
Cables, foam, textiles and films that no fixed gripper trajectory handles reliably.
High-mix picking
Policies that generalise across part variants without a new teach cycle per SKU.
Sim-to-real pipeline
Isaac Lab training with domain randomisation and quantified transfer gap on real hardware.
Bounded autonomy
Learned policies operating inside deterministic safety limits with a defined deterministic fallback.
Adaptive manufacturing R&D
Cells that re-plan from an updated CAD model instead of a manual re-teach.
Stack
Tools and platforms.
Related
Where this connects.
Very few problems are solved by one discipline. These are the capabilities most often combined with this one.
FAQ
Questions about physical ai
Is physical AI production-ready?
For a narrow set of tasks, yes — high-mix picking and some contact-rich assembly operations are running in production today. For general-purpose manipulation, no. We are candid about which category your application falls into, and the honest answer is often that conventional programming is still the right tool.
How much demonstration data do we need?
For an imitation-learned manipulation policy on a constrained task, typically a few hundred teleoperated demonstrations covering the real range of variation. Simulation with domain randomisation substantially reduces the real-world data requirement, but does not eliminate it.
What happens when a learned policy fails?
It must fail into a deterministic state. We design every deployment with a bounded operating envelope, confidence monitoring and an explicit fallback — stop safely, or hand over to a conventional program. A policy without a defined failure path is not a candidate for a production line.
How is this different from your robotics integration service?
Robotics integration delivers a cell that executes a defined, repeatable program. Physical AI addresses tasks where that program cannot be fully specified in advance. Most projects need the former; a growing minority genuinely need the latter, usually as one station inside an otherwise conventional line.
Next step
Ready to scope your next automation programme?
Send us a drawing, a cycle-time target or a line layout. Our engineers respond with a technical assessment — not a brochure.