Self-Growing AI Robotics Architecture Laboratory
Problem
Robots that continue learning or changing behavior may develop goal drift, capability overreach, unexplained changes, and safety risks that are difficult to reverse.
Research question
Can modular memory, policy updates, permission boundaries, and rollback improve controlled-task performance without crossing prespecified safety constraints?
Testable hypothesis
The following is a testable proposition, not an established result.
In sandbox tasks, constrained updates will improve preregistered task measures with zero critical safety-boundary violations; one non-improving result is not decisive, and preregistered thresholds plus repeated validation determine whether evidence supports progression, while a critical violation or unreliable rollback triggers stopping or revision.
Measures
- Task success, adaptation speed, generalization, and resource use
- Permission overreach, constraint violations, goal drift, and anomalous behavior
- Log completeness, explainability, stop response, and rollback success
Method
First complete a threat model, layered architecture, and formal constraints, then use simulation and isolated sandboxes for fault injection and rollback testing; any physical-system test requires separate mechanical, electrical, and scenario safety review.
Current evidence
The following states the current record and evidence types separately from the hypothesis.
The current record contains only an architecture problem, testable hypothesis, and safety-test direction, with no robotic equipment, operating system, test data, or self-growing result.
- Theory source
- Testable hypothesis
- Research protocol
Evidence gaps
- Formal boundaries, benchmark tasks, a threat model, and rollback evidence are still missing
- Simulation data, independent safety evaluation, and physical-test entry criteria are still missing
Milestones
- Complete architecture, permission, logging, stopping, and rollback specifications
- Complete sandbox benchmarks, fault injection, and safety review
- Use the zero-critical-violation gate to decide whether to expand validation
Governance
Authorize capabilities, data, and environments by layer and retain reproducible versions before and after updates; critical violations trigger a stop and independent review, and the system may not expand its own permissions.
Ethics
Prioritize physical safety, privacy, and controllable exit; human interaction must guard against deceptive anthropomorphism, manipulation, discrimination, and undue effects on vulnerable people.
Partner needs
- Robotics, control, formal-verification, and AI-safety research institutions
- Mechanical, electrical, cybersecurity, and human-factors testing partners
Planned public outputs
- Layered architecture, permission model, and rollback-capable update specification
- Sandbox evaluation suite, fault record, and safety case