{"ID":2859127,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05547","arxiv_id":"2510.05547","title":"ARRC: Advanced Reasoning Robot Control - Knowledge-Driven Autonomous Manipulation Using Retrieval-Augmented Generation","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.05547","url_pdf":"https://arxiv.org/pdf/2510.05547v1","authors":"[\"Eugene Vorobiov\",\"Ammar Jaleel Mahmood\",\"Salim Rezvani\",\"Robin Chhabra\"]","published":"2025-10-07T03:20:10Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}