Controlled Robot Language with Frame Semantics (FrameCRL) for Autonomous Context-Aware High-Level Planning
Dang M. Tran, Fujian Yan, Qiang Zhang, Yinlong Zhang, Hongsheng He
Abstract
This paper proposes a configurable and scalable framework based on Controlled Robot Language with Frame Semantics (FrameCRL) for plan generation. Given natural language instructions, FrameCRL constructs an equivalent formal semantic formulation in the form of discourse representation structures (DRS). Imperative verbs are extracted from the semantic structures as keys to anchor relevant semantic frames from FrameNet, and the selected semantic frames are used to construct goal statements in planning language. Non-imperative statements are further analyzed to generate object specifications and the initial state of the planning problem. These generated statements are then merged into a single planning script, which can be solved directly by the integrated planner. The performance of FrameCRL was evaluated on various natural language corpora and compared with large language models (LLM) based methods in plan generation. The results demonstrated the outperformance of FrameCRL in generating high-quality plans and its capability to handle large context scenarios. The FrameCRL was also tested on pick-and-place tasks using a dual-arm robot and it showcased a robust performance in linguistic understanding.
BibTeX
@inproceedings{icra2025_controlledrobotl,
title = {Controlled Robot Language with Frame Semantics (FrameCRL) for Autonomous Context-Aware High-Level Planning},
author = {Dang M. Tran and Fujian Yan and Qiang Zhang and Yinlong Zhang and Hongsheng He},
booktitle = {ICRA 2025},
year = {2025}
}