Energy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy

Abdul Basit; Muhammad Shafique

cs.RONov 5, 2026

Energy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy

Authors: Abdul Basit, Muhammad Shafique

Organizations: eBRAIN Lab, New York University Abu Dhabi, Abu Dhabi, UAE

Abstract

Open-vocabulary object detection (OVD) lets robots search for run-time object queries, but fixed-rate inference spends embedded compute on low-value frames during idle motion, map revisits, or stable viewpoints. This paper presents an energy-aware OVD scheduler for low-power mobile robots. The scheduler uses odometry, map change, and detector confidence to switch among low-, mid-, and high-power YOLO-World-S modes on a Jetson Orin Nano. We evaluate the energy-quality trade-off on an AgileX LIMO using 40 real navigation trials: two target objects, five placements per target-policy pair, and four perception policies. Across bottle and cup trials, the scheduler records 8/10 mission success compared with 7/10 for each fixed-rate baseline, while reducing mean Jetson input power (a Jetson-side power-draw metric) by 5.2% relative to an 8 Hz high-rate baseline and lowering achieved OVD call rate by 34.0%. It does not minimize mission-window Jetson input energy because longer missions can erase instantaneous power savings. A separate 60 s stationary microbenchmark isolates the perception workload: the scheduler lowers mean power from 8.045 W to 7.301 W and energy from 484.7 J to 438.1 J relative to the 8 Hz baseline. These results support robot-state-aware OVD scheduling as a promising power-management mechanism with competitive task reliability, while showing that mission-window Jetson input energy must be read together with task progress and runtime.

Figures & tables

Work	OVD	Robot	Adapt.	$R/E$	Task
OWL-ViT/GLIP/GDINO [ 2 , 3 , 4 ]	✓	$\times$	$\times$	$\times$	$\times$
Detic/YOLO-World [ 5 , 6 ]	✓	$\times$	$\times$	$\blacktriangle$	$\times$
VLMaps/LM-Nav/ROVER [ 7 , 10 , 11 ]	$\blacktriangle$	✓	$\times$	$\times$	$\blacktriangle$
OV scene graphs [ 8 , 9 ]	✓	$\blacktriangle$	$\times$	$\times$	$\blacktriangle$
Active/NBV perception [ 12 , 13 ]	$\times$	✓	✓	$\blacktriangle$	✓
NoScope/Chameleon [ 14 , 15 ]	$\times$	$\times$	✓	$\blacktriangle$	$\times$

TABLE I: Qualitative coverage of representative related work.

Fig. 1: Proposed energy-aware OVD runtime. Robot sensing and mapping produce motion, map-change, confidence, and target-distance signals. The tri-state scheduler maps these signals to LOW/MID/HIGH detector modes, while the logging pipeline records Jetson input power, mission-window energy, inference count, success, TTD, and mode occupancy.

Parameter	Value	Purpose
$r_{s}$	20 Hz	Scheduler update rate
$f_{L}/f_{M}/f_{H}$	1.5/4/8 Hz	Requested OVD rates
$\ell_{\Omega}$	4.0 m	Local grid-window side
$v_{\mathrm{idle}}$	0.03 m/s	Idle-motion threshold
$\tau_{H}$	0.02	Map-change threshold
$T_{\mathrm{low}}$	2.0 s	Stable interval before LOW

TABLE II: Scheduler parameters used in the reported experiments.

Fig. 2: Real-robot arena with fixed LIMO start pose and bottle/cup target placements reused across policies.

Policy	Succ.	$\bar{P}_{J}$	$E_{\mathrm{m}}$	Run	TTD succ	Hz	L/M/H
		(W)	(J)	(s)	(s)		(%)
Base-8Hz	7/10	9.35	866.9	92.8	25.9	4.74	0/0/100
Base-5Hz	7/10	8.92	1019.8	114.2	53.6	3.39	0/100/0
Base-2Hz	7/10	8.38	1155.0	140.4	0.9	1.48	100/0/0
Proposed	8/10	8.86	1114.0	125.9	10.9	3.12	25/60/15

TABLE III: Real-robot navigation summary over bottle and cup trials (ten trials per policy). low-, mid-, and high-power modes.

Fig. 3: Policy dashboard over the 40 navigation trials. The proposed scheduler has the highest aggregate success count and lower mean Jetson input power than Base-8Hz, while Jetson input energy is dominated by runtime.

Fig. 4: Mission runtime versus Jetson input energy for all 40 trials. Circles are successes, crosses failures, and open diamonds condition means; dashed lines show constant-power contours.

Fig. 5: Compact proposed-scheduler trace from the 60 s scripted stationary run. Colored regions show state, orange ticks show detector calls, the blue curve is Jetson input power, and the red dashed line is mean power.

Fig. 6: Representative adaptive navigation trace showing Jetson input power/energy, scheduler state and detector calls, detector confidence with the 0.25 threshold, and path length.

Fig. 7: Qualitative video-frame progression for representative bottle and cup searches. Blue R marks the robot and red T marks the target; LOW/MID/HIGH labels are qualitative stage estimates, not synchronized scheduler labels.

Policy	$\bar{P}_{J}$	$E$	Hz	#Inf.	L/M/H
	(W)	(J)			(%)
Base-8Hz	8.045	484.7	6.67	402	0/0/100
Base-5Hz	7.578	456.6	4.40	265	0/100/0
Base-2Hz	6.847	410.8	1.90	114	100/0/0
Proposed	7.301	438.1	3.37	202	50/38/13

TABLE IV: Stationary microbenchmark over the power-integration window.

Fig. 8: Object-level success and state occupancy, mapped to LOW/MID/HIGH power modes. In the top plot, solid bars denote bottle and hatched bars denote cup.

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XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling

Tony Tran, Richie R. Suganda, Bin Hu

Department of Research Computing University of Houston Houston, TX 77479 · Department of Electrical and Computer Engineering University of Houston Houston, TX 77479 · Department of Engineering Technology University of Houston Houston, TX 77479

Energy-Efficient ML Edge Inference Efficient Object Detection Hardware-Aware NAS Object Detection Neural Architecture Search

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Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure. We address these challenges with an energy-adaptive framework that combines an energy-aware XiResOFA search space, a two-stage energy estimator, and iterative search to identify a single energy-efficient base architecture. We then apply compound scaling to transform this base design into the XiYOLO family across deployment budgets, enabling interpretable accuracy-energy tradeoffs under sparse hardware measurements. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines. On PascalVOC, the medium XiYOLO model reaches 86.15 mAP50 while reducing energy relative to YOLOv12m by 20.6% on GPU and 35.9% on NPU. On COCO, XiYOLO reduces energy relative to YOLOv12 by up to 53.7% on GPU and 51.6% on NPU at the small scale. The proposed two-stage estimator also improves sample efficiency over a joint predictor under few-shot adaptation with only 2-20 target-device samples.

Abstract

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