Posture-Aware Active Shelf Inspection under Velocity–Posture Tracking Constraints
A Go2 robot plans only the viewpoints it can actually reach.
Abstract
Capstone of ORIA’s Onboard volume on legged active perception. On a quadruped, body height and pitch reshape the sensor viewpoint, so the planner selects velocity, height, and pitch to reduce shelf-occupancy uncertainty while staying inside the velocity–posture command region the controller can actually track — the achievable-viewpoint envelope. Sweeping 270 commands for a simulated Unitree Go2, only 86 are trackable, concentrated at moderate heights and failing at extreme crouch and at high-height, high-speed commands. Under matched viewpoints, non-nominal posture exposes 13–17.5 percentage points more audit cells within the reach window, and across five layouts a posture-enabled planner removes 42% of change-risk-weighted entropy versus 26% at nominal posture. A myopic (H=1) planner performs comparably, indicating that posture availability — not planning horizon — accounts for most of the gain. Accepted to ICAI’26 (Springer Nature); full paper to be linked upon publication.
- Active perception
- Legged robots
- Viewpoint planning
- Posture control
- Information gain
- Warehouse inspection
Full paper linked upon publication.