TL;DR
The paper introduces Agentic Robot, a brain-inspired framework utilizing a structured coordination protocol called SAP, which improves long-horizon robotic manipulation by enhancing reasoning, execution, and error recovery in autonomous systems.
Contribution
It presents a novel brain-inspired architecture with SAP for structured coordination, enabling better reasoning, execution, and error handling in embodied robotic agents.
Findings
Achieved 79.6% success rate on LIBERO benchmark, outperforming previous models.
Demonstrated improved performance and interpretability in sequential manipulation tasks.
Enabled autonomous error recovery through a temporal verifier component.
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 Procedure (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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. Overall, this is a well-written paper with good motivation and experiment validation. 2. A lot of details are shown in the appendix.
1. No real-world deployment experiments. 2. More datasets and baselines should be included like VLABench, Cot-VLA, COA-VLA, pi 0.5, Groot, etc. https://openaccess.thecvf.com/content/ICCV2025/papers/Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper.pdf https://arxiv.org/pdf/2501.15830 https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_CoT-VLA_Visual_Chain-of-Thought_Reasoning_for_Vision-Language-Action_Models_CVPR_2025_paper.pdf ht
- Originality: The paper introduces a clearly defined coordination protocol—Standardized Action Procedure (SAP)—that operationalizes a brain-inspired perception–planning–execution–verification loop for embodied agents. Unlike prior sequential planners or end-to-end VLAs, SAP formalizes subgoal-level verification and recovery as first-class components, yielding a modular and enforceable protocol that goes beyond ad-hoc prompting or implicit success checks. - Quality: The framework is carefully e
- The evaluation is overly simple and limited: it relies solely on the LIBERO benchmark and includes no real-world experiments. - It omits comparisons with stronger open-source baselines, such as pi0.5.
- Clear modularity and interpretability. SAP defines explicit interfaces and a closed perception–planning–execution–verification loop, making component roles and information flow easy to reason about. - Verification as a control signal. The periodic VLM verifier enables early error detection, retries, and subgoal-level termination, which is well-motivated for long horizons.
- Limited technical novelty. The core contribution is a coordination protocol that combines existing ingredients (LLM/LRM planner, VLA executor, VLM verifier) rather than introducing new learning algorithms or model architectures; much of the lift comes from structuring the pipeline. - Baselines skew toward end-to-end VLAs. Comparisons largely pit SAP against executor-only policies; the paper lacks head-to-head evaluations versus hierarchical planner + skill/executor systems (e.g., code-as-poli
1. The paper features a clear modular design: the planner–executor–verifier loop is explicitly defined and highly interpretable, drawing a meaningful analogy to biological and cognitive systems. 2. The paper is clearly organized and well written, making it easy to follow the methodology and key ideas.
1. The novelty of the work is limited. Each module is built on existing systems, and the main contribution lies in their integration rather than model-level innovation. Much of the work focuses on prompt engineering and system composition. The overall pipeline is very similar to many existing agent-based frameworks, showing little distinction from prior work [1,2,3]. 2. A key limitation is the lack of real-world validation. All experiments are performed in simulation, while physical robot deploy
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
