Probe2Act: Knowing When to Stop Probing Articulated Objects

A robot can discover how an unfamiliar drawer or hinged door moves by probing it, but every additional interaction consumes budget. We study a practical decision: when does the observed motion justify stopping identification and beginning execution? Probe2Act combines an analytic common-path estimator with a calibrated stopping rule. The estimator retains displacement between probes, so individually weak responses can jointly reveal a local motion direction; the rule stops when the direction's calibrated angular radius meets a declared tolerance, or abstains when the budget is exhausted. Under exchangeability of complete trajectory bundles, the finite-prefix construction bounds the marginal joint event of executing with excessive unsigned direction error. In Panda simulation with supplied contact and link tracking, using 100 calibration and 100 test geometries, Probe2Act reduces clean interaction cost from 12 to 1.87 queries, with 97.5% versus 98.5% task success. Under combined pose noise, it uses 6.78 queries while both methods achieve 98% success. None of its 800 nominal executions has an initial direction error above $15^{\circ }$. Matched comparisons and ablations identify which estimation and uncertainty choices support these savings. Tracking shifts reduce uncertainty-set coverage despite accurate selected directions, identifying a boundary for transfer to real sensing.