Human–robot collaboration
A person interrupts a task, changes the destination, or reaches for an object. Evaluate whether the robot recognizes the new intent and coordinates the handover.
Helm Physical AI / Physical digital twins
Judgment for robots
in a human world.
Helm builds behavioral environments that teach and test AI judgment. Physical digital twins bring site context, human behavior, and changing task state into robotics simulation and evaluation.
Embodied judgment / Shared workspaceHelm Labs
The complete Physical AI portfolio
From physical data
to coordinated operations.
Connect embodied and decision models, site reconstruction, human behavior, robot inference, evaluation, and coordination across people, robots, and agents.
The decisions between instructions and action
Rehearse what happens
when the task changes.
Begin with high-level interaction and recovery. Define the observation, available actions, and expert response before comparing robot policies.
Manipulation & recovery
A grasp fails or an object is no longer where it was observed. Evaluate whether the system refreshes its understanding, selects a valid recovery, or asks for help.
What to inspect
Observation age · Recovery choice · Final task state
Mobile robots in shared spaces
A route becomes occupied or a station is unavailable. Evaluate whether the policy respects local priorities and finishes the intended task as conditions change.
INSIDE A PHYSICAL WORLD
A small change. A different decision.
Explore three illustrative variations. Compare the response and the evidence needed to judge it.
ENVIRONMENT / 01TOP VIEW
STORAGE A
STORAGE B
STATION B
START
OCCUPIED ZONE
REPLAN REQUIRED
Proposed pathTask environment / illustration
THE EXPERIMENT
Can the task survive a layout change?
Move a storage unit into the route. Compare clearance, reachability, and whether the policy chooses another path.
- VARY
- Aisle width and object placement
- INSPECT
- Task completion, contact, and clearance
Illustrative scenario selector. No physics engine or robot is running.
From human experience to repeatable evaluation
Build the world
around the decision.
Combine the task environment with human behavior and an explicit standard for what should happen next.
- 01 / Capture the context
Bring the task and the people.
Start from an existing simulation or scoped site evidence. Include demonstrations, operating rules, and reviewed examples of intervention.
- 02 / Author the variations
Change what matters.
Vary intent, timing, object state, and environment conditions. Keep initial states and human responses reproducible.
- 03 / Compare & validate
Inspect choices and consequences.
Compare policies on held-out cases. Check expert agreement and task outcomes, then identify what requires observation on hardware.
- 04 / Reuse the evidence
Make the next release better informed.
Retain failure cases as regression tests. Scope approved corrections for training and measure improvement on independent tasks.
A place in your robotics stack
The human context
around your robot policy.
Helm’s focus is the behavior, task logic, and evaluation surrounding embodied decisions. Scope the connection to your simulator, policy, and robot interfaces around the task.
See the shared Helm foundationHelm / Environment & judgment layerPeople · Task state · Expert standards
↕Behavioral variations, outcome checks, and reviewed corrections
Your robot policyObservations → decisions → actions
↕Compare models and policy versions through an agreed interface
Your simulator & hardwarePhysical dynamics · Sensing · Control
Validate physical assumptions and stopping behavior on the actual system
Environment and interface support are scoped per engagement. Simulation evidence informs hardware testing; it does not establish safe deployment.
Physical AI, explained
A clear place to start.
What is a behavioral environment for robotics?
A task environment that includes how people act and respond: their intentions, interruptions, corrections, and changing instructions. Evaluation connects the robot’s decisions to an expert standard and an observable outcome.
What can a robotics team buy first?
A scoped environment pack for one embodiment and task family. Agree the task interface, human-behavior variations, baseline comparison, replayable traces, and human and hardware validation plan before the engagement.
How does this fit with a physics simulator or world model?
Physics and world models address the environment and its dynamics. Helm’s focus is the human behavior, task requirements, and judgment criteria within that environment. The integration and physical assumptions are defined for the chosen task.
Can the environments support training as well as evaluation?
Training access and data rights can be scoped separately. Preserve independent evaluation tasks and check transfer against human and hardware observations before interpreting better simulation scores as real-world improvement.
Build with Helm
Bring the task your robot
needs to get right.
One embodiment. One environment. A repeatable test of judgment.
Talk to Helm about Physical AI