Robotics & Physical AI

53 stories · publishing
Updated Sep 2, 2026
AIU research department

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The lead

Sep 2, 2026

Robotics & Physical AIThe Robot ReportmajorAug 31, 2026

Skild AI unveils S1, a robot foundation model that picks up a task from one video

Skild AI released S1, its flagship "omni-bodied" robot foundation model, on 31 August 2026.

What it means for your work

One-video, in-context task acquisition is the demo the whole robot-foundation-model field is chasing — and no benchmark score or deployment count has been published against it yet.

△ vendor-claimed · therobotreport.comRead it at therobotreport.com

What today means

Our read on the items that move something. The reporting is everyone's; this part is ours.

  1. The tool-calling layer that made software agents useful is being pointed at instruments and machines; if it holds, the same harness that drives an API drives a microscope or a robot arm.Anthropic opens a research preview of a shared standard for agents to drive lab and factory hardwareThe Robotics & Physical AI beat · Anthropic

  2. Humanoid programmes have mostly been demos; a raise this size tied to a stated end-of-year production date is a schedule the rest of the sector now gets measured against.XPeng’s robotics unit raises over $900M at a $6.3B valuation ahead of humanoid mass productionThe Robotics & Physical AI beat · XPeng

  3. The retrofit-onto-fleet-you-already-own pattern keeps winning the money in construction robotics — the productivity claim is the vendor’s, but the deployment model means a contractor can pilot it without a capital purchase.Gravis Robotics raises $200M from SoftBank to retrofit excavators into autonomous machinesThe Robotics & Physical AI beat · The Robot Report

2 new findings here overnight. 53 here in all.

What this department watches

The charge

This department watches AI that moves things in the physical world: robot foundation models, the humanoid and mobile-robot programmes reaching real sites, the on-device compute that runs them, and the operators and capital behind the deployments. Its standing bias is toward what actually gets installed and kept running — a fleet in service on a real floor is evidence; a stage demonstration is not.

  • Robot foundation models, and which labs are training them
  • Humanoid and mobile-robot programmes reaching real facilities
  • On-device compute and the cost curve underneath it
  • Fleet operations, uptime, and safety practice around people
  • Robotics-as-a-service economics, and who is funding it
  • Standards and regulation for robots working alongside staff

The questions it keeps open

6 standing questions

These are the open questions this department works. They are questions, not conclusions — what comes back gets published with its sources attached.

  • Which robot platforms are actually being deployed at scale right now, and in which physical environments — and which ones are being quietly pulled back out?
  • When a fleet's software or firmware updates, what breaks, and how are operations teams staging rollouts so a facility doesn't stop?
  • What do the real maintenance economics look like — mean time between failures, parts availability, technician load — versus what the vendor's numbers claim?
  • Where does the safety and compliance floor sit for humans working alongside these systems, and what changed in the standards this cycle?
  • What is a robot vendor's support model worth in practice, and how do operators structure contracts and escalation for uptime they can defend?
  • Which capability jumps — cheaper perception, better manipulation, general-purpose models on the robot — change what a facility can automate at all, not just faster?

How the research runs

The process
1 · Read the real sources

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2 · Carry the method and the source

A finding is only useful if you can check it. Every item carries the source it came from and the date that source published, and links straight out to the original — so a claim can be verified rather than taken on trust.

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3 · Feed the coursework

What this department reads is what keeps the coursework current — and the coursework decides what is worth watching next. The two are meant to inform each other, which is why the curriculum below is on this page rather than somewhere else.

The curriculum behind it

3 courses on aiuni.tech
The major

AI + Robotics & Automation Operations

Deploy, manage, and optimize robot systems in real facilities — the operations work that makes automation actually run.

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