analiza aiqbench
The paper treats robot manipulation as an AI orchestration problem: an "agentic" layer plans tasks, tracks state, and recovers from failures, and it is benchmarked against Vision-Language-Action models. The reported gains (22.5% and 25.0% task success over VLA and hierarchical baselines) come from the authors' own evaluations, so they need independent replication. For AI readers, the useful question is whether explicit structured memory and scene graphs can offset the precision and generalization limits of end-to-end VLA models.
UniManip: Open-World Robotic Manipulation via Agentic Operational Graphs
arXiv:2602.13086v2 Announce Type: replace Abstract: Achieving general-purpose robotic manipulation requires robots to bridge high-level semantic intent with low-level physical interaction in unstructured environments. However, zero-shot generalization remains challenging: end-to-end Vision-Language-Action (VLA) models often lack the precision required for long-horizon tasks, while traditional hierarchical planners suffer from semantic rigidity under open-world variations. We present UniManip, a unified framework centered on an Agentic Operational Graph (AOG), an explicit cognitive and operational backbone for task orchestration, state tracking, execution verification, and closed-loop failure recovery. High-level semantic intents are grounded into actionable primitives via a physically grounded scene graph that provides geometric affordances, contact-aware interaction parameters, and collision-free trajectory synthesis. By coupling a high-level Agentic Layer for task orchestration with a low-level Scene Layer for dynamic state representation, UniManip continuously aligns abstract planning with geometric constraints, enabling robust execution. Unlike static pipelines, UniManip operates as a dynamic agentic loop: it instantiates object-centric scene graphs from unstructured perception, parameterizes these representations into collision-free trajectories via a safety-aware local planner, and exploits structured memory to autonomously diagnose and recover from execution failures. UniManip achieves state-of-the-art performance on LIBERO-Pro, while extensive real-world experiments demonstrate robust zero-shot generalization across diverse tasks, embodiments, and dexterous hands, outperforming leading VLA models and hierarchical baselines by 22.5% and 25.0% in task success rate, respectively. Open-source project page: https://henryhcliu.github.io/unimanip.
Abstract:Achieving general-purpose robotic manipulation requires robots to bridge high-level semantic intent with low-level physical interaction in unstructured environments. However, zero-shot generalization remains challenging: end-to-end Vision-Language-Action (VLA) models often lack the precision required for long-horizon tasks, while traditional hierarchical planners suffer from semantic rigidity under open-world variations. We present UniManip, a unified framework centered on an Agentic Operational Graph (AOG), an explicit cognitive and operational backbone for task orchestration, state tracking, execution verification, and closed-loop failure recovery. High-level semantic intents are grounded into actionable primitives via a physically grounded scene graph that provides geometric affordances, contact-aware interaction parameters, and collision-free trajectory synthesis. By coupling a high-level Agentic Layer for task orchestration with a low-level Scene Layer for dynamic state representation, UniManip continuously aligns abstract planning with geometric constraints, enabling robust execution. Unlike static pipelines, UniManip operates as a dynamic agentic loop: it instantiates object-centric scene graphs from unstructured perception, parameterizes these representations into collision-free trajectories via a safety-aware local planner, and exploits structured memory to autonomously diagnose and recover from execution failures. UniManip achieves state-of-the-art performance on LIBERO-Pro, while extensive real-world experiments demonstrate robust zero-shot generalization across diverse tasks, embodiments, and dexterous hands, outperforming leading VLA models and hierarchical baselines by 22.5% and 25.0% in task success rate, respectively. Open-source project page: this https URL.
Robotics (cs.RO)
arXiv:2602.13086 [cs.RO]
(or arXiv:2602.13086v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2602.13086 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 305 cuvinte
Subiecte conexe
TriPilot-FF: Coordinated Whole-Body Teleoperation with Force Feedback
arXiv · 2 sept. 2026
Multi-Axis Selective Decoupling Framework for Free-Floating Space Manipulators
arXiv · 24 sept. 2026
RUL-Aware RRT*: Degradation-Balanced Motion Planning for Robotic Manipulators
arXiv · 5 oct. 2026
Optimising multipartite entanglement manipulation in quantum networks
arXiv · 16 oct. 2026