Evaluation depends on task and embodiment
Success varies by task, robot, sensors, and action space. Benchmarks must control for each.
The runtime forphysical AI.
Evaluate. Adapt. Deploy.
We help robotics companies evaluate, fine-tune, and deploy robot policies on their own hardware.
POLICY → ROBOT → REAL WORLD01 / PHYSICAL AI INFRASTRUCTURE
THE PROBLEM01 — 02
Policies meet
real-world constraints.
Different robots. Different interfaces. Limited onboard compute.
Policy and robot interfaces differ
VLA inputs and actions must map to robot observations, coordinate frames, joint order, and control APIs.
Failed rollouts need processing
Failures require triage and annotation before they become policy-compatible fine-tuning data.
Edge hardware limits inference
Policies must meet onboard memory, throughput, and control-loop latency budgets without losing task performance.
OUR SOLUTION02 — 03
Everything between
the policy and the robot.
A focused deployment stack for robotics teams.
01↗
EVALUATION & POST-TRAINING
FieldArena
Benchmark VLA policies across tasks and robot setups.
EXPLORE FIELDARENA ↗
02⟳
POLICY-TO-ROBOT ADAPTATION
FieldAdapt
Map policy observations and actions to each robot’s sensors, frames, and control API.
EXPLORE FIELDADAPT ↗
03⌁
ON-ROBOT INFERENCE
FieldEdge
Optimize policy inference for the target robot’s compute and control-loop budgets.
EXPLORE FIELDEDGE ↗FIND THE RIGHT POLICYADAPT IT TO YOUR ROBOTRUN IT ON YOUR HARDWARE
REAL ROBOT DEMOS04 / IN THE FIELD
Policies in
motion.
Watch learned policies handle tabletop manipulation tasks on physical robots.
01 / BLOCK STACKING
02 / MULTI-TASK MANIPULATION“
Built for robotics teams that want better AI without building every part of the stack themselves.
Request early access ↗RUNNING PILOTS03 — 04
WORK SPANS
- Autonomous material handling
- Robotic welding
- Planetary rover research
- Robot manipulation research
EARLY ACCESS04 — 04
Let’s put physical AI
to work.
Tell us what you’re building and where deployment gets difficult.