Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Long-horizon robotic manipulation poses significant challenges for autonomous
systems, requiring extended reasoning, precise execution, and robust error
recovery across complex sequential tasks. Current approaches, whether based on
static planning or end-to-end visuomotor policies, suffer from error
accumulation and lack effective verification mechanisms during execution,
limiting their reliability in real-world scenarios. We present Agentic Robot, a
brain-inspired framework that addresses these limitations through Standardized
Action Procedures (SAP)--a novel coordination protocol governing component
interactions throughout manipulation tasks. Drawing inspiration from
Standardized Operating Procedures (SOPs) in human organizations, SAP
establishes structured workflows for planning, execution, and verification
phases. Our architecture comprises three specialized components: (1) a large
reasoning model that decomposes high-level instructions into semantically
coherent subgoals, (2) a vision-language-action executor that generates
continuous control commands from real-time visual inputs, and (3) a temporal
verifier that enables autonomous progression and error recovery through
introspective assessment. This SAP-driven closed-loop design supports dynamic
self-verification without external supervision. On the LIBERO benchmark,
Agentic Robot achieves state-of-the-art performance with an average success
rate of 79.6\%, outperforming SpatialVLA by 6.1\% and OpenVLA by 7.4\% on
long-horizon tasks. These results demonstrate that SAP-driven coordination
between specialized components enhances both performance and interpretability
in sequential manipulation, suggesting significant potential for reliable
autonomous systems. Project Github: https://agentic-robot.github.io.
