Sri Sai Rakesh Nakkilla

arXiv2026

Cross-Stage Sensorimotor Perception Scheduling and Sparse Map Encoding for Efficient Edge Embodied Navigation

Embodied agents must close a perception-to-action loop on embedded hardware under tight latency, memory, and energy budgets, making deployment a system-level co-design problem rather than a model-accuracy problem. We study this challenge for modular Object Goal Navigation (ObjectNav), where our profiling shows semantic mapping dominates per-step latency while goal prediction dominates peak memory. We formulate edge embodied navigation deployment as a budget-constrained design-space problem and introduce two orthogonal optimization knobs: SKIP, an adaptive sensorimotor scheduler that formalizes safe skipping as a bounded map-impact criterion and learns a lightweight predictor to estimate it from cheap sensor cues at each \texttt{FORWARD} step, exposing a principled quality-efficiency knob (depth-based updates are always retained); and SCOUT, a sparse-context encoder that couples submanifold sparse convolutions on active map regions with a lightweight dense context stream. On HM3D across server and embedded platforms, SKIP+SCOUT delivers up to 1.7x end-to-end speedup, 50.5% lower peak memory, and 7.1% higher SPL than the dense baseline at the selected operating point, outperforming naively smaller perception backbones. SKIP transfers to a second modular pipeline (PONI) with near-lossless performance and remains robust under depth-sensor noise. Together, SKIP+SCOUT expose a family of device-aware Pareto operating points for edge physical AI systems.

cs.RO↗

arXiv2025

Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables

Conventional stress monitoring relies on episodic, symptom-focused interventions, missing the need for continuous, accessible, and cost-efficient solutions. State-of-the-art approaches use rigid, silicon-based wearables, which, though capable of multitasking, are not optimized for lightweight, flexible wear, limiting their practicality for continuous monitoring. In contrast, flexible electronics (FE) offer flexibility and low manufacturing costs, enabling real-time stress monitoring circuits. However, implementing complex circuits like machine learning (ML) classifiers in FE is challenging due to integration and power constraints. Previous research has explored flexible biosensors and ADCs, but classifier design for stress detection remains underexplored. This work presents the first comprehensive design space exploration of low-power, flexible stress classifiers. We cover various ML classifiers, feature selection, and neural simplification algorithms, with over 1200 flexible classifiers. To optimize hardware efficiency, fully customized circuits with low-precision arithmetic are designed in each case. Our exploration provides insights into designing real-time stress classifiers that offer higher accuracy than current methods, while being low-cost, conformable, and ensuring low power and compact size.

cs.LG↗