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Retrieval-Augmented Robots via Retrieve-Reason-Act

arXiv:2603.02688v1 Announce Type: new Abstract: To achieve general-purpose utility, we argue that robots must evolve from passive executors into active Information Retrieval users. In strictly zero-shot settings where no prior demonstrations exist, robots face a critical information gap, such as the exact sequence required to assemble a complex furniture kit, that cannot be satisfied by internal parametric knowledge (common sense) or past internal memory. While recent robotic works attempt to use search before action, they primarily focus on retrieving past kinematic trajectories (analogous to searching internal memory) or text-based safety rules (searching for constraints). These approaches fail to address the core information need of active task construction: acquiring unseen procedural knowledge from external, unstructured documentation. In this paper, we define the paradigm as Retrieval-Augmented Robotics (RAR), empowering the robot with the information-seeking capability that bri

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Izat Temiraliev, Diji Yang, Yi Zhang · · 1 min read · 9 views
#cs.AI #cs.RO

arXiv:2603.02688v1 Announce Type: new Abstract: To achieve general-purpose utility, we argue that robots must evolve from passive executors into active Information Retrieval users. In strictly zero-shot settings where no prior demonstrations exist, robots face a critical information gap, such as the exact sequence required to assemble a complex furniture kit, that cannot be satisfied by internal parametric knowledge (common sense) or past internal memory. While recent robotic works attempt to use search before action, they primarily focus on retrieving past kinematic trajectories (analogous to searching internal memory) or text-based safety rules (searching for constraints). These approaches fail to address the core information need of active task construction: acquiring unseen procedural knowledge from external, unstructured documentation. In this paper, we define the paradigm as Retrieval-Augmented Robotics (RAR), empowering the robot with the information-seeking capability that bridges the gap between visual documentation and physical actuation. We formulate the task execution as an iterative Retrieve-Reason-Act loop: the robot or embodied agent actively retrieves relevant visual procedural manuals from an unstructured corpus, grounds the abstract 2D diagrams to 3D physical parts via cross-modal alignment, and synthesizes executable plans. We validate this paradigm on a challenging long-horizon assembly benchmark. Our experiments demonstrate that grounding robotic planning in retrieved visual documents significantly outperforms baselines relying on zero-shot reasoning or few-shot example retrieval. This work establishes the basis of RAR, extending the scope of Information Retrieval from answering user queries to driving embodied physical actions.

Executive Summary

This article proposes a novel paradigm for robotics, known as Retrieval-Augmented Robotics (RAR), which enables robots to actively retrieve relevant information from unstructured corpora to bridge the gap between visual documentation and physical actuation. The authors define a Retrieve-Reason-Act loop, where robots retrieve relevant visual procedural manuals, ground abstract 2D diagrams to 3D physical parts, and synthesize executable plans. The authors validate this paradigm on a challenging long-horizon assembly benchmark, demonstrating that grounding robotic planning in retrieved visual documents significantly outperforms baselines relying on zero-shot reasoning or few-shot example retrieval. This work establishes the basis of RAR, extending the scope of Information Retrieval from answering user queries to driving embodied physical actions.

Key Points

  • ▸ The article proposes a novel paradigm for robotics, Retrieval-Augmented Robotics (RAR), which enables robots to actively retrieve relevant information from unstructured corpora.
  • ▸ The authors define a Retrieve-Reason-Act loop, where robots retrieve relevant visual procedural manuals, ground abstract 2D diagrams to 3D physical parts, and synthesize executable plans.
  • ▸ The authors validate the RAR paradigm on a challenging long-horizon assembly benchmark, demonstrating significant improvement over baselines.

Merits

Strength in Addressing Information Gap

The article addresses a critical information gap in strictly zero-shot settings, where robots face challenges in acquiring unseen procedural knowledge from external, unstructured documentation.

Innovative Retrieve-Reason-Act Loop

The proposed Retrieve-Reason-Act loop is a novel and innovative approach to enabling robots to retrieve relevant information, ground abstract diagrams to physical parts, and synthesize executable plans.

Demerits

Limited Scope of Unstructured Corpus

The article assumes a limited scope of unstructured corpus, which may not be representative of real-world scenarios where robots may face complex and diverse information sources.

High Computational Complexity

The proposed Retrieve-Reason-Act loop may incur high computational complexity, which may be challenging to implement in real-time applications.

Expert Commentary

The article proposes a novel and innovative approach to enabling robots to retrieve relevant information from unstructured corpora, which addresses a critical information gap in robotics. The proposed Retrieve-Reason-Act loop is a significant contribution to the field of Information Retrieval in Robotics. However, the article assumes a limited scope of unstructured corpus, which may not be representative of real-world scenarios. Additionally, the proposed approach may incur high computational complexity, which may be challenging to implement in real-time applications. Nevertheless, the article has significant practical and policy implications for the development of robotics and artificial intelligence.

Recommendations

  • ✓ Future research should focus on expanding the scope of unstructured corpus to include more diverse and complex information sources.
  • ✓ Developing more efficient and scalable algorithms for the Retrieve-Reason-Act loop is essential for real-time applications.

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