From Prompt to Robot Program
An engineering talk on the boundary between AI planning and physical execution: capability grounding, review, permissioned execution, telemetry, and replay.
Direct answer
Direct answer: Physical AI and robotics
This talk explains how to keep AI planning and physical execution separate enough to operate safely in practice: ground the plan in what the machine can actually do, review it, execute with explicit permissions, record telemetry, and make recovery and replay possible. It draws on building RobotGPT at Orangewood Labs and on the WareMax deterministic warehouse benchmark. It shows engineering patterns and does not offer a safety assessment of any audience’s systems.
The question
“Our audience needs to understand the boundary between AI planning and physical execution. What can an engineering talk demonstrate responsibly?”
Who it is for
Robotics, manufacturing, warehousing, and industrial-AI conferences and engineering events.
From language plan to edge execution
Abstract
What the talk covers
Language models can turn an instruction into a plausible plan for a robot in seconds. Plausible is not enough when the plan becomes motion. A fluent plan can reference capabilities a cell does not have, ignore a constraint a human operator would know, or fail halfway with nobody able to explain what happened. This talk sets out the systems map that keeps physical AI operable: grounding plans in known machine capabilities, reviewing them before execution, permissioning actions at the edge, recording telemetry, and making recovery and replay possible. It draws on building RobotGPT at Orangewood Labs, which reduced collaborative-robot programming time by approximately 10× in that specific context, and on WareMax, a deterministic warehouse benchmark with byte-identical replay. The talk is explicit about its boundary: it teaches engineering patterns and does not assess the physical safety of any installation.
The audience leaves able to
- →Why the boundary between AI planning and physical execution needs explicit design
- →How capability grounding and review reduce plausible but unworkable plans
- →Which decisions stay at the edge, and which actions require explicit permission
- →How telemetry and deterministic replay make physical AI explainable and recoverable
Audiences
Formats
- Conference talk
- Keynote
- Technical deep dive
- Panel
- Webinar
- Podcast
What you leave with
Talk abstract, a cloud-to-edge responsibility diagram, and a safety-boundary discussion.
- 01 An abstract tailored to the event’s industry and technical depth
- 02 A cloud-to-edge responsibility diagram showing where planning, review, and execution sit
- 03 A checklist for capability grounding, permissioned execution, telemetry, and replay
- 04 Short and long speaker biographies from the speaker kit
How it runs
Structure and format
- Duration
- 30–45 minutes as a talk; 60 minutes with questions
- Delivery
- In person in the UK, travel by agreement, or remote
- Participants
- Robotics and automation engineers, manufacturing and warehouse technology teams, and technical leaders
Language is not a robot program
Why a fluent plan can still ask a machine to do something it cannot do, or should not do in that cell.
Grounding and review
Constraining plans to known capabilities and putting a person or deterministic check between plan and motion.
Permissioned execution at the edge
Where cloud planning stops, what runs locally, and which actions need explicit permission.
Telemetry, recovery, and replay
Recording enough to explain a run, recover from a fault, and reproduce behaviour, with deterministic simulation as a test bed.
Audience assumptions
- →Familiarity with robotic cells, automation systems, or industrial software
- →No machine-learning specialism is assumed
Evidence
What this draws on
RobotGPT at Orangewood Labs
Reduced collaborative-robot programming time by approximately 10× in that specific programming context, with cloud-to-edge and computer-vision systems around it.
WareMax
A deterministic warehouse-robotics benchmark for task allocation, reward design, causal delay attribution, and byte-identical replay.
Inspect the source ↗Provisional patent filings
90+ provisional filings across Hike and Orangewood Labs in AI, computer vision, robotics, messaging, and consumer systems. Provisional filings, not granted patents.
Scope and limits
What this is not
- —The talk does not constitute a safety assessment and offers no safety certification; independent physical-safety assessment is outside its scope.
- —The RobotGPT result of approximately 10× less programming time applies to that specific collaborative-robot programming context only.
- —WareMax is a deterministic simulation benchmark; its results are not measurements from a deployed warehouse.
If the need is different
Common questions
Answers before you commission
What can a talk on physical AI demonstrate responsibly?+
Architecture patterns, failure modes, and how responsibility is divided between planning, review, and execution, using public benchmarks and past delivery experience. It cannot vouch for the safety of a particular installation.
What was RobotGPT?+
A system built at Orangewood Labs that reduced collaborative-robot programming time by approximately 10× in that specific programming context.
Is the talk about humanoid robots?+
No. It focuses on industrial and collaborative robotics, warehousing, and the cloud-to-edge systems around them.
Can the talk include a demonstration?+
A simulation-based walkthrough using deterministic replay is possible where the venue supports it; live hardware is not assumed.
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