Robotics
Robot Self-Trust
Code: ANX-RO-001
Investigate whether an autonomous robot can determine when its own perception of reality has become unreliable or potentially compromised by reasoning about contradictions across its sensors and expected physical behavior.
Why it matters
An autonomous robot that cannot tell when it is being deceived is a hazard. Perception is only trustworthy if the machine can reason about contradictions in its own inputs — a camera showing a clear path while LiDAR reports an obstacle, or an IMU recording motion the controller never commanded. A Robotic Self-Trust Layer gives the robot a way to doubt itself and act accordingly.
Research gap
Sensor fusion for robustness has a long history, but explicitly reasoning about contradictions as a clue that perception is compromised — producing a Perception Reliability Score that feeds behavior — remains underexplored, particularly as a defense against spoofed or corrupted sensor streams.
Research questions
- 01 What formal notion of self-trust captures reliability across conflicting sensor streams?
- 02 Can contradiction patterns between camera, LiDAR, IMU, and expected physical trajectories reliably detect corrupted or spoofed perception?
- 03 How should a robot behave as its Perception Reliability Score degrades — halt, degrade gracefully, or fall back to a safe mode?
- 04 How fast must cross-sensor trust analysis run to remain useful for real-time control?
Suggested technologies
Required skills
Team and supervisor
2-4 researchers, supervised by Muhammad Qasim.
Suitable for: Robotics, Computer Vision, AI, Cybersecurity, Embedded.
Expected deliverables
- Cross-sensor trust analysis framework for autonomous robots
- Evaluation on simulated and real-world perception-contradiction scenarios
- Research paper defining and assessing the Robotic Self-Trust Layer
Work on this problem
Apply to the lab and this problem becomes your supervised research project.