Precision
Small differences in placement and timing can determine whether a task succeeds.
The human-skill training layer
PlainHand is developing the software, evidence systems, sensing, and future data instruments that could help dexterous machines learn more from human expertise.
Early research stage · Performance advantages are not yet claimed
NOW Phase One evidence program
DESIGNED Matched baselines + reproducible measurement
See the research statusPlainHand is not building another generic robot company.
We are building the layer through which dexterous machines could learn skilled physical intelligence from us.
See how the company fits togetherThe problem
Dexterity is becoming a training problem.
Physical skill is not a visible path alone. It includes force, contact, timing, correction, and recovery, which ordinary recordings often miss.
Contact awareness
Machines must respond to the physical world, not only recognize what it looks like.
Fine motor control
Useful dexterity spans approach, contact, adjustment, and release.
Reliability
A learned skill matters only when it remains dependable as conditions change.
What we are building now
A research program with visible claim boundaries.
Current work starts with controlled software experiments. Each extension has to earn its place against a strong baseline.
01DESIGNED
Phase One
A governed test of whether coherence can make recorded multimodal demonstrations more useful.
Explore this research direction ↗02UNDER INVESTIGATIONAdaptive Sensor Attention
Keep every sense available while changing how strongly each one influences a prediction.
Explore this research direction ↗03RESEARCH EXTENSIONHierarchical coherence
Test fixed, adaptive, and conventional structures under the same held-out rules.
Explore this research direction ↗04RESEARCH DIRECTIONAgent structure discovery
Ask whether bounded computational agents can discover useful organization without adding unjustified complexity.
Explore this research direction ↗05WORKING REPRESENTATION HYPOTHESISMovement vocabulary and packets
Test whether small pieces of physical interaction can become reusable units that transfer across objects and tasks.
Explore this research direction ↗06PROPOSED HYPOTHESISLearning from a few successful attempts
Ask what reusable relationships can be learned from the variation among only a few successes.
Explore this research direction ↗07RESEARCH EXTENSIONTwo directions of learning
Test whether physical interaction can produce a policy from which a predictive world model is later recovered.
Explore this research direction ↗08RESEARCH EXTENSIONRecovery under irreversible contact
Test whether useful structure survives contact events that permanently change the state of the world.
Explore this research direction ↗How the system grows
Each evidence layer earns the next.
The long-term platform compounds software evidence, selective sensing, proprietary physical data, and correction without assuming every layer will pass its gate.
Public-data evidence
Test the learning thesis with compatible recorded data.
Selective sensing
Build only what the evidence says is missing.
Human-data glove
Capture synchronized contact, force, pose, and motion.
Robot data instrument
Execute, measure, and generate new interaction data.
Correction loop
Turn failures and human corrections into new training records.
Why the data matters
More information from every physical interaction.
North-star measureCapability
per demonstration
per demonstration
The ambition is not simply to collect more demonstrations. It is to test whether each interaction can teach the system more useful, transferable physical structure.
Stable intervalMeaningful changeNew interval
Research proof standard
Proof before claims.
A mechanism matters only if it improves a defined outcome under fair comparison and survives an attempt to disprove it.
Strong baselines
Compare against matched conventional alternatives, not weak strawmen.
Sealed confirmation
Keep confirmation data separate from mechanism discovery and tuning.
Portability
Require the core selection effect to survive in a second compatible architecture.
Negative results preserved
Stop, narrow, or defer a claim when the evidence does not support it.
Intended domains
Different machines. The same dexterity gap.
These are fields where physical skill matters and small errors carry real cost. They are intended applications, not validated PlainHand deployments.
Humanoid robotics
Careful, repeatable work in environments built for people.
Precision manufacturing
Tasks where small changes in placement, timing, or contact can alter the outcome.
Laboratory automation
Delicate, variable, and procedure-driven physical workflows.
Infrastructure + field work
Specialized work in settings that are difficult to standardize.
Why now
The intelligence arrived. The hands still need teaching.
Foundation models are moving into robotics. Hardware is improving. Public multimodal datasets and accessible compute make the learning architecture testable now. The remaining constraint is whether machines can learn precise physical work with enough context, efficiency, and reliability to be useful.
Human skill becomes teachable structure
The PlainHand approach
Make the architecture clear. Make the evidence visible.
PlainHand turns physical skill into a testable learning system. The diagrams show how demonstrations, sensor signals, evidence gates, and transfer tests fit together. The status ledger separates planned work from demonstrated results.
Read the evidence statusThe company
Built around one question.
What if human physical expertise could become a durable resource for machine learning?
PlainHand turns that question into software experiments, evidence infrastructure, selective sensing, and future data-generation tools. The company is led by founder and CEO Levi Ezagui.
The research de-risks the company direction; the company compounds the research into durable technology and data assets.
Explore the companyWork with PlainHand