TIVA
Runtime temporal integrity for perception-driven robots.
Prithvi Bhat · prithvibhat19@gmail.com
The problem
Under peak load, perception lags while health checks stay green. Motion continues. Logs look fine. Failures show up as wrong picks, near misses, or days lost on intermittent floor issues.
What TIVA does
TIVA evaluates whether observations and perception outputs remain temporally valid before they influence downstream planning and actuation: whether an observation should enter inference, and whether published output should reach planners and actuators. Perception models remain unchanged; no retraining required.
Stage 1 today: temporal observation (JSONL traces), validation (KPI and classification), and governed replay on recorded perception workloads (videos below). Traces make overload visible; governance holds or skips when output is no longer contract-compliant. Next: live edge governor on the same contract.
Where it applies
- Warehouse AMRs: docking, obstacles, congestion under load
- Industrial arms: stale picks when vision lags
- Drones: late detections under compute contention
How it fits
Two checks: before inference (admit or skip) and before actuation (forward, hold, or withhold). Timing health is derived from the temporal contract in runtime traces.
Why not timestamps, QoS, or watchdogs alone?
| Existing approach | Limitation |
|---|---|
| Timestamp threshold | Detects age but provides no runtime governance |
| Dropping frames | Reduces backlog but does not govern published outputs |
| ROS 2 QoS | Manages communication quality, not temporal validity for actuation |
| Watchdogs | Detect failures after they occur; do not govern degraded operation |
Validation
Lab replay on recorded workloads demonstrates governance under stale perception: stale published output triggers a downstream false brake without TIVA; with governance, the stream holds until output is temporally valid (conveyor demo). Replay comparison shows wait-or-skip under stress on a real detector. Controlled lab conditions; mechanism proof, not field ROI.
Design partners
Teams seeing timing-related perception failures under load: short calls to understand the workload; recorded-trace evaluations later by mutual agreement.