Tenslam Vision — Physical AI Infrastructure

Build Reliable AI for the Physical World

Turn real-world behavior into structured physical episodes for evaluation, simulation and learning.

Tenslam Vision helps transform video, model outputs and physical-system data into information developers can inspect, compare and eventually replay in simulation.

Explore the Technology

Early-stage company. Product direction is being validated with developers.

Episode #0142 · grasp_transferIllustrative sample data
01 · Video / robot log
  • 00:02.0Worker reaches for object
  • 00:04.0Contact detected
  • 00:05.0Model predicts: grasp success
  • 00:06.0Object slips
  • 00:06.4Failure detected
02Physical episode
agent
arm_01
object
container_A
contact
t=4.0s
outcome
slip
03Failure intelligence

Grip lost during transition

conf.
0.87
orient.
Δ 18°
v12
✓
v13
✗
04Simulation / learning

Replay: simulation-ready scenario

Concept: vary grip and orientation, re-test v13.

Concept visualization. Values are examples, not measurements from a deployed system.

The problem

Physical AI has a debugging problem.

Developers can increasingly build perception and policy models. Understanding why a physical system fails in a real environment is still hard. Real-world interactions are messy, and the evidence is scattered across raw video, logs and sensor streams.

Multimodal

Video, robot logs, telemetry and model outputs live in separate tools and formats.

Temporal

Failures unfold over time. A single frame or a single metric rarely explains what went wrong.

Hard to reproduce

A failure seen once in a real environment is difficult to inspect, share or test against again.

Hard to compare

It is unclear whether a new model version improved or regressed physical behavior.

What Tenslam Vision does

From physical behavior to structured intelligence.

Pose and skeleton estimation can be one perception method among several. It is an input to the system, not the product.
  1. 01

    Observe

    Ingest video, model outputs and system data.

  2. 02

    Structure

    Represent agents, objects and time.

  3. 03

    Understand

    Identify actions, contacts and transitions.

  4. 04

    Evaluate

    Compare expected and observed behavior.

  5. 05

    Replay

    Turn episodes into reproducible scenarios.

  6. 06

    Improve

    Compare model versions and track regressions.

Images are conceptual illustrations, not outputs from a deployed system.

Try it now · Free

AI Video Quality Check

Upload a short clip or use your camera. The check runs in your browser and tells you whether the video is usable for pose-based analysis: lighting, blur and full-body visibility.
Try the Quality Check →Experimental. Part of Labs; not medical or coaching advice.

Core abstraction

Physical Episodes

A Physical Episode is a structured record of what happened during a real or simulated physical interaction. It can carry more useful information than raw video or joint coordinates alone.

Input

  • Video
  • Model output
  • Telemetry and robot logs
  • Simulation traces

Episode

  • • Agent
  • • Object
  • • Action
  • • Contact
  • • Timeline
  • • Prediction
  • • Outcome
  • • Uncertainty

Output

  • Searchable structured record
  • Evaluations and timelines
  • Machine-readable representation

The episode schema is under active design and will change with customer discovery.

Evaluation

Find where physical intelligence breaks.

For robotics and AI engineers: understand not only whether a model failed, but when, why and whether it is getting better.
Temporal failure detectionResearch direction
Behavior comparisonResearch direction
Regression analysisResearch direction
Anomaly and event discoveryResearch direction
Model-version comparisonPlanned
Failure clusteringPlanned
Confidence analysisPlanned
Scenario searchPlanned

Real world ↔ simulation

Real-world failures should become test cases.

The long-term idea is to connect real episodes with simulation, so difficult cases can be reproduced and varied instead of being seen once and forgotten.
  1. 1Capture a hard real-world episode
  2. 2Structure it
  3. 3Approximate the scenario in simulation
  4. 4Vary conditions
  5. 5Test newer models
  6. 6Catch regressions

Faithful real-to-simulation reconstruction is an open research problem. We are exploring approximate, evaluation-oriented scenarios, not perfect reconstruction.

Potential workflows

Where this could be useful.

Robotics development

Inspect and compare real-world robot behavior.

Physical AI evaluation

Test model versions against structured episodes.

Simulation and synthetic data

Seed scenarios from real episodes.

Human-robot and human-object interaction

Represent contact and handover events.

Industrial physical systems

Review anomalies across logs and video.

Human-performance analysis is an experimental application, explored in Labs.

Technical architecture

A layered direction, labelled honestly.

Each layer is tagged with its current status. Only perception is usable today, as an in-browser pose demo in Labs.

L1

Perception

Identify relevant agents, objects and motion.

Available now

L2

Temporal understanding

Understand actions and changes through time.

Prototype

L3

Interaction

Human-object, robot-object and environment interactions.

Research direction

L4

Physical events

Contacts, transitions, constraints, anomalies.

Research direction

L5

Evaluation

Expected versus observed behavior and failure modes.

Research direction

L6

Simulation bridge

Episodes that can later be reproduced or varied in simulation.

Planned

L7

Structured output

Searchable events, timelines, machine-readable records.

Planned

Full technical overview →

Compute

Why accelerated computing is relevant.

Evaluating video and multiple model versions across many episodes is compute-heavy, and simulation-based testing is too. We are exploring GPU-accelerated inference and simulation tooling where it fits. No integration with these tools is shipped today.
GPU inference (e.g. Triton) · exploringOpenUSD interchange · exploringIsaac Sim / Isaac Lab · designed to integrate with

NVIDIA, Triton, Omniverse, OpenUSD and Isaac are trademarks of their respective owners. Tenslam Vision is not affiliated with or endorsed by NVIDIA.

Current stage

Early development, built in the open.

Tenslam Vision is currently in early development. We are working with real-world physical data and building the first version of our evaluation and physical-episode infrastructure. The product direction will follow what we learn from developers and from technical validation.

Talk to us

Building Physical AI? We want to understand your hardest evaluation failures.

We are looking to speak with robotics, simulation and Physical AI teams working on difficult real-world evaluation problems.
  • Robot failure logs
  • Difficult edge cases
  • Real-vs-simulation gaps
  • Behavior regression
  • Video and log synchronization
  • Evaluation workflows
team@tenslamvision.com