Cross-Stage Sensorimotor Perception Scheduling and Sparse Map Encoding for Efficient Edge Embodied Navigation
For researchers deploying embodied AI on resource-constrained hardware, this work provides principled optimization knobs that yield significant efficiency gains without sacrificing performance.
The paper tackles the deployment of embodied navigation agents on edge devices under tight resource constraints. It introduces SKIP, an adaptive sensorimotor scheduler, and SCOUT, a sparse-context encoder, achieving up to 1.7x speedup, 50.5% lower peak memory, and 7.1% higher SPL compared to dense baselines on ObjectNav.
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.