arXiv AI
Energy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy
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LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
arXiv:2609.23974v1 Announce Type: new Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
arXiv Computer Vision
Oct 27
Thermal-Headroom Routing for Efficient Robot Perception in Extreme Heat
arXiv:2610.35231v1 Announce Type: cross Abstract: Adaptive robot perception systems must balance detector accuracy, inference latency, and energy consumption, particularly when high ambient temperatu...
Hugging Face Trending Papers
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LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through visual perception. A representative example is Human...
arXiv AI
Oct 15
OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
OxyGen introduces a unified KV cache management system for Vision‑Language‑Action (VLA) models that run multiple tasks in parallel, such as manipulation, conversation, and memory construction. By treating the KV cache as a shared resource, it eliminates redundant prefill operations and enables cross‑frame continuous batching, leading to significant speedups—up to 3.7× faster than isolated execution—while maintaining high language throughput and action frequency on both RTX 4090 and Jetson AGX Thor platforms. The approach is validated across several backbones and expert configurations, including deployment on a humanoid robot with on‑board Jetson hardware.
arXiv AI
Jul 15
Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
arXiv:2607. 12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks.
arXiv AI
Aug 18
Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.