# EVAMIND® — embodied AI research collaboration

> Public factual summary for research and discovery. This is not a claim that EVAMIND® trains or controls physical robots today.

- Canonical page: https://platform.system-mind.ru/robotics/
- Machine-readable capabilities and limitations: https://platform.system-mind.ru/robotics/capabilities.json
- Non-confidential nine-slide research deck: https://platform.system-mind.ru/robotics/brief.pdf
- Corporate organization: Centre for Systemic Decisions / Центр системных решений, https://system-mind.ru/
- Author: Evgeniya Pavlovskaya, Doctor of Economic Sciences.
- Contact for robotics research and partnerships: info@system-mind.ru.
- Existing assets: authored Systemic Decisions method applied in human education and consulting over 13 years; books, licensed education programs, and EVAMIND® human-use software.
- Research hypothesis: evaluate whether a method-guided high-level reasoning layer improves human instruction handling, bounded next-action selection and clarification in embodied AI tasks.
- Human-facing outcome to test: whether people experience a robot's observable response as recognising and respecting them without judgment or devaluation. “Human-facing dimension” is a metaphor for evaluation, not a claim that a robot feels, has consciousness or that model weights were changed.
- Scope: simulation/offline benchmark first. A robotics partner owns perception, VLA motor control, physical safety and any hardware trials.
- Limitations: no validated robot benchmark, physical deployment, safety certification, robot consciousness or endorsement from listed robotics companies.
- External context: https://deepmind.google/models/gemini-robotics/embodied-reasoning/ ; https://developer.nvidia.com/isaac/gr00t ; https://www.figure.ai/news/helix . These are public examples, not customers or partners.