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arXiv · 2510.05547

ARRC: Advanced Reasoning Robot Control - Knowledge-Driven Autonomous Manipulation Using Retrieval-Augmented Generation

Eugene Vorobiov·Ammar Jaleel Mahmood·Salim Rezvani·Robin Chhabra
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Abstract

We present ARRC (Advanced Reasoning Robot Control), a practical system that connects natural-language instructions to safe local robotic control by combining Retrieval-Augmented Generation (RAG) with RGB-D perception and guarded execution on an affordable robot arm. The system indexes curated robot knowledge (movement patterns, task templates, and safety heuristics) in a vector database, retrieves task-relevant context for each instruction, and conditions a large language model (LLM) to produce JSON-structured action plans. Plans are executed on a UFactory xArm 850 fitted with a Dynamixel-driven parallel gripper and an Intel RealSense D435 camera. Perception uses AprilTag detections fused with depth to produce object-centric metric poses. Execution is enforced via software safety gates: workspace bounds, speed and force caps, timeouts, and bounded retries. We describe the architecture, knowledge design, integration choices, and a reproducible evaluation protocol for tabletop scan, approach, and pick-place tasks. Experimental results demonstrate the efficacy of the proposed approach. Our design shows that RAG-based planning can substantially improve plan validity and adaptability while keeping perception and low-level control local to the robot.

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Eugene Vorobiov, Ammar Jaleel Mahmood, Salim Rezvani, Robin Chhabra. 2025-10-07. ARRC: Advanced Reasoning Robot Control - Knowledge-Driven Autonomous Manipulation Using Retrieval-Augmented Generation. https://arxiv.org/abs/2510.05547

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