Lise DelobelPhysical AI

Physical AI · Robotics · Autonomous Systems

Physical AI as the New Frontier for AI Pioneers

Research, insights and field experience at the frontier of Physical AI — from humanoid robotics to autonomous systems and embodied intelligence.

  • MIT IDSS
  • PMP
  • Harvard
  • OpenUSD (NCP-OUSD)
  • Bilingual FR/EN
Lise Delobel climbing a mountain face, symbolising the frontier of Physical AI

About

I am a Physical AI consultant and project manager specialising in autonomous systems, robotics and embodied intelligence. After a career spanning large-scale industrial projects across Energy (Oil & Gas), Finance (CIB/Trading) and IT, I made a deliberate transition toward Physical AI — the frontier where artificial intelligence meets the physical world. I lead the LOGIE-AI consortium (France 2030), consult on GenAI programs at Vallourec, and am completing MIT's Physical AI program and OpenUSD certification.

Workshop session mapping a Physical AI programme

Research & Publications

Peer-reviewed papers, white papers and technical reports on Physical AI and autonomous systems

Robotics

Physical AI Frontiers — The Pause That Sounds Like Thinking: Conversational Timing in Social Robots

July 2026

Why human turn transitions average 208 milliseconds, why one laboratory study found users preferred about 700 milliseconds from a robot, and how endpointing — not model speed — dominates the delay people actually experience in conversational robotics.

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Robotics

Physical AI Frontiers — The Last Hundred Microns: Precision Assembly When Clearance Is Smaller Than Workcell Uncertainty

July 2026

When the combined uncertainty of robot, tooling, fixturing and parts exceeds assembly clearance, precision has to be recovered through the interaction loop: the geometry of jamming, admittance control, compliant end-effectors, and simulation-trained insertion policies that now transfer zero-shot at industrial tolerances.

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Robotics

Physical AI Frontiers — The Reality Gap: Simulation-trained policies, synthetic data, and what actually transfers from the simulator to the factory floor

July 2026

How simulation moved from test bench to training data factory, why the gap between simulated and real behaviour is now the central engineering risk in robotics, and what managers need to know about sim-to-real transfer, learned dynamics and scaling laws for dexterity.

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Robotics

Physical AI Frontiers — The Last Inch: Kinesthetic Sensing, Dense Clutter, and the Quantity a Camera Cannot Measure

July 2026

A field analysis of Amazon's contact-rich stow robotics deployment: how kinesthetic feedback corrects vision-only estimates, why compressibility is the variable no camera can measure, and what it means for industrial project managers buying embodied AI.

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Digital Twins

Digital Twins and AI/ML for Predictive Maintenance in Industrial Systems

2025

How physics-grounded digital twins combined with machine learning shift maintenance from scheduled to genuinely predictive across heavy industry.

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Robotics

Embodied Intelligence: Project Management Challenges in Physical AI Deployments

2025

Field lessons on governance, safety validation and cross-disciplinary teams when intelligent systems leave the screen and enter the workplace.

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Space

AI-Enabled Spacecraft Health Management

July 2026

Telemetry analytics, digital twins and bounded autonomy in the risk architecture of a satellite fleet: how ML anomaly detection, operational twins and tiered machine authority are moving spacecraft risk management from insurance transfer toward engineering resilience.

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AI/ML

Physical AI Frontiers — One Scene, Many Solvers: OpenUSD as a Foundation for Physical AI Simulation Pipelines

July 2026

Why a shared scene description layer is becoming the connective tissue of robotics simulation, synthetic data and sim-to-real transfer — and why standardised description does not mean standardised physics: what composition reliably buys you, where solver semantics diverge, and how to adopt OpenUSD without believing the marketing.

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Insights

Field notes, analysis and perspectives on the Physical AI ecosystem

March 2026Robotics

What I learned visiting MIT AgeLab and Stanford's robotics labs

Two labs, two philosophies of embodiment — and a shared conviction that the hard problems are no longer purely computational.

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February 2026Physical AI

Why Physical AI is the next frontier for industrial project managers

The discipline that delivered refineries and trading platforms is exactly what robotics programmes are missing today.

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November 2025Robotics

LOGIE-AI: building AI companion robots under France 2030

Inside a national R&D consortium: consortium governance, hardware timelines and the reality of shipping embodied AI.

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Field Experience

2025–present

LOGIE-AI · France 2030 R&D consortium

Project Manager — AI companion robot platform

2024–present

Vallourec

GenAI PMO — three concurrent AI programs (ECHO+GENIUS, PLP TenderBoost, Safety AI)

April 2025

MIT IDSS

Data Science & Machine Learning Certificate

Summer 2026

MIT Professional Education

Physical AI Program

Speaking & Media

  • Paris Robotics Week — Panel

    Embodied intelligence in European industry

    June 2026

  • France 2030 · LOGIE-AI Public Session

    Companion robotics for ageing societies

    March 2026

  • Vallourec Digital Days

    Running three GenAI programmes at once

    October 2025

Available for keynotes, panels and expert commentary on Physical AI, robotics and autonomous systems. Bilingual FR/EN.

Contact

Based in Paris · Available internationally

I respond within 24 hours