robotics
The Robot Chooses a Skill for When It Will Actually Start
Time-indexed success models plan around 100-to-300-millisecond delays instead of pretending the observed scene will wait.
Summary
Time-indexed success models plan around 100-to-300-millisecond delays instead of pretending the observed scene will wait.
A receding-horizon planner predicts whole-task success for both a skill and a future launch time. In a simulated sliding-puck task, it beat a static competence model by 2.8 to 6.2 percentage points across 100 to 300 milliseconds of latency and was substantially better calibrated. On a real UR5e catching a rolling ball after an injected 0.2-second delay, the static model predicted 99 percent success for a prespecified subset but observed 63 percent; the time-indexed model predicted 56 percent. The hardware evidence is narrow, but it exposes the cost of ignoring latency.
Why it matters
Time-indexed success models plan around 100-to-300-millisecond delays instead of pretending the observed scene will wait.
Limits and context
No additional limitation was separately recorded.
Key claims
Time-indexed success models plan around 100-to-300-millisecond delays instead of pretending the observed scene will wait.
Evidence: source-2026-11-07-006
Sources
- arXiv preprint 2611.07964, original v1arXiv · primary research
Corrections
: Publication note: This November 7 edition was assembled before its scheduled 9:00 AM America/Detroit reveal but is being published late after recovery from a local build interruption. Its original reporting, image-generation times and scheduled publishAt are preserved.