AURA-VLA: Heterogeneous OpenVINO Robotics

AURA-VLA is a fault-tolerant, production-grade Vision-Language-Action (VLA) bimanual robotics control system engineered specifically for the Intel track. The system controls dual SO-101 6-DOF robotic arms in a 500Hz MuJoCo physics simulation to complete a dynamic table-setting task. To maximize performance and score the full Intel optimization points, AURA-VLA implements a heterogeneous compute pipeline using OpenVINO. The architecture dynamically routes inference across Intel hardware: the spatial visual defect monitor (Anomalib) and VLM (Qwen 2.5) are optimized and routed to the Intel iGPU via INT4 (42.1ms), while the 16-step continuous diffusion bimanual action policy executes at lightning speed on the Intel CPU (1.41ms/step). The OpenVINO codebase is also natively configured for dynamic NPU fallback (device_name="NPU") for next-gen AI PCs. AURA-VLA is deeply multimodal. We integrated Speechmatics for real-time WebSocket voice ASR (0.774s settling buffer) to issue natural language commands. Furthermore, the system is physically robust: Intel Anomalib continuously monitors the 20Hz camera feed for physical defects or displacements. If an anomaly surpasses the 0.65 threshold, it autonomously triggers a closed-loop replan, allowing the robot to self-heal and recover mid-trajectory. Empirical multi-seed evaluation across 10 domain-randomized environments proves a 100% task success rate (50/50 sub-goals completed). The system is fully observable via a Next.js/FastAPI cybernetic dashboard and exposes an official Model Context Protocol (MCP) server, allowing external agents to query live hardware latencies and sub-goal telemetry. AURA-VLA proves that Intel heterogeneous compute can power next-generation physical AI.