PLAIN HAND
PlainHand is building verified robotics technology that creates trustworthy, independently provable records of machine actions. The company focuses on responsibility, confidence, and collaboration across the robotics industry, with five utility patents in process covering processes for verified robotics. Its approach enables teaching robots from actual events, catching mistakes before incidents occur, and lowering insurance costs through measurable risk reduction.
- Robotics
- Artificial Intelligence
- Software Only
- Hardware
Funding
Founders
Founder details are not available yet.
Product
Problem
Physical skill acquisition for dexterous machines is constrained by insufficient training data. Ordinary recordings of human demonstrations miss critical information like force, contact, timing, correction, and recovery, which are essential for reliable fine motor control and contact awareness. As a result, robots struggle to perform reliably when conditions change, limiting their usefulness in real-world physical tasks.
Solution
PlainHand is developing the software, evidence systems, sensing, and future data instruments that could help dexterous machines learn more from human expertise. The company is building a layer through which dexterous machines could learn skilled physical intelligence from humans, rather than another generic robot company. Its current work focuses on a research program with visible claim boundaries, starting with controlled software experiments where each extension must earn its place against a strong baseline. The long-term platform compounds software evidence, selective sensing, proprietary physical data, and correction without assuming every layer will pass its gate. The staged roadmap includes public-data evidence, selective sensing, a human-data glove capturing synchronized contact, force, pose, and motion, a robot data instrument for execution and measurement, and a correction loop that turns failures and human corrections into new training records. The north-star measure is capability per demonstration, testing whether each interaction can teach the system more useful, transferable physical structure.
Target Audience
Primary customers include robotic machine operators, insurance providers, and organizations requiring verifiable accountability for physical AI systems, particularly where third-party verification of machine actions is needed. The company also targets the broader dexterous robotics sector seeking to improve physical skill acquisition from human expertise.
Features
- Three-stage architectural approach: commitment, measurement, and evidence, with tamper-evident expectations recorded before action and sealed records produced after action
- Staged roadmap: public-data evidence, selective sensing, human-data glove, robot data instrument, and correction loop, with each layer earning the next
- Human-data glove designed to capture synchronized contact, force, pose, and motion data from human demonstrations
- Focus on physical skill elements beyond visible paths: force, contact, timing, correction, and recovery
- Methodology based on matched baselines and reproducible measurement with visible claim boundaries
- Five utility patents in process covering verified robotics processes for trustworthy, independently provable accounts of machine actions
- Subject to a filed USPTO intellectual-property application