Presentation
Late Breaking Results: Energy-Aware Scheduling of Open-Vocabulary Detection on Low-Power Mobile Robots
DescriptionOpen-vocabulary object detection enables language-driven object search for mobile robots, but on embedded GPUs a fixed 8 Hz detector can dominate the mission energy budget. We propose an
energy-aware runtime that couples a tri-state scheduler (SLEEP/EXPLORE/TRACK at 1.5/4/8 Hz) with dual-precision switching between INT8 and FP16 YOLO-World-S on a Jetson Orin Nano robot. A tegrastats-based logger provides mission energy and a task-level energy-per-detection metric. Across 48 real-robot missions, the scheduler cuts mission energy by 7.5% relative to a fixed 8 Hz FP16 baseline while using 33% fewer inferences, and achieves similar success rate and time-to-detect.
energy-aware runtime that couples a tri-state scheduler (SLEEP/EXPLORE/TRACK at 1.5/4/8 Hz) with dual-precision switching between INT8 and FP16 YOLO-World-S on a Jetson Orin Nano robot. A tegrastats-based logger provides mission energy and a task-level energy-per-detection metric. Across 48 real-robot missions, the scheduler cuts mission energy by 7.5% relative to a fixed 8 Hz FP16 baseline while using 33% fewer inferences, and achieves similar success rate and time-to-detect.
Event Type
Work in Progress
TimeMonday, July 275:26pm - 5:27pm PDT
LocationExhibit Hall