NVIDIA Manufacturing United States

Noble Machines

Noble Machines needed to train, evaluate, and deploy a general-purpose industrial robot fast enough for real customer environments without relying on slow, physical-only testing or cloud-dependent inference.

15 people
18 months
50 people
four years

Reported impact

Business outcome

Noble Machines cut its development timeline by nearly 3x, shipped Moby to Asia within 18 months of founding, and achieved that pace with a team of about 15 people instead of a much larger conventional robotics program.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Noble Machines uses the NVIDIA Isaac GR00T stack as the backbone for developing Moby, its general-purpose industrial robot, across simulation, training, evaluation, and onboard deployment. Engineers import mechanical designs and customer facility twins into NVIDIA Isaac Sim, use the Newton Physics Engine to model contact-rich behaviors such as grasping and lifting, and scale policy training with Isaac Lab plus VR teleoperation data. Pretraining runs on NVIDIA H100 GPUs, post-training and scene simulation use NVIDIA RTX 6000 PRO and GeForce RTX 5090 GPUs, and thousands of digital Moby instances can run in parallel before policies ever reach hardware. On the robot itself, NVIDIA Jetson Thor processes camera and sensor data and runs foundation-model inference locally, while partners such as ADLINK, Solomon, and Schaeffler help ruggedize hardware and adapt workflows for semiconductor facilities.

Data flow

Digital facility models and teleoperation examples feed Isaac Sim and Isaac Lab training workflows, large GPU clusters refine policies across thousands of simulated Mobys, and the trained models are deployed onto Jetson Thor for real-time perception and control on the physical robot.

Solution architecture

8 components · 3 layers
  1. Compute
    • NVIDIA Isaac Sim Hosts the digital robot and customer-facility environments used for simulation and workflow design.
    • Newton Physics Engine Simulates contact-rich actions such as lifting, grasping, and moving objects before deployment.
    • NVIDIA H100 GPUs Accelerate pretraining for Moby's robotics models.
    • NVIDIA RTX 6000 PRO GPUs Support post-training and large-scale simulation workloads.
    • NVIDIA GeForce RTX 5090 GPUs Run high-volume simulation jobs depending on scene complexity.
  2. Serving
    • NVIDIA Isaac GR00T 1.7 Provides the vision-language-action foundation model Noble Machines refines for Moby's behavior.
    • NVIDIA Jetson Thor Processes sensor data and runs robotics foundation-model inference onboard the physical robot.
  3. Orchestration
    • NVIDIA Isaac Lab Supports whole-body-control training and sim-to-real deployment workflows.

Architecture clues

  • Jetson Thor keeps inference onboard so Moby does not rely on external communication for decisions
  • Moby is being adapted to semiconductor material-handling workflows and future construction use cases
  • The stack combines simulation, teleoperation data, policy evaluation, and sim-to-real deployment

Evidence from the source

Accelerated Moby's development timeline by nearly 3x, from an expected four years to 18 months.
Achieve these milestones with a team of about 15 people, versus a conventional approach that would require at least 50 people.
Isaac Sim and Isaac Lab let Noble Machines run thousands of digital Moby instances in parallel.
NVIDIA Jetson Thor processes sensor data and runs robotics foundation-model inference onboard.
Transparency Evidence & methodology 100/100 reference value · 92% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    Noble Machines customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/noble-machines/
    Source type
    Vendor-published customer story
    Published
    Aug 24, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Humanoids Daily

    Noble Machines — company profile, robots and news | Humanoids Daily

    Independent secondary
    https://www.humanoidsdaily.com/companies/noble-machines

    The company profile lists 'Headquarters' as 'Sunnyvale' in the 'United States'.

    Source type
    Structured company profile
    Supports
    • Region

Date evidence

  • Published on source page Aug 24, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.