In this section
@article{Chen:2025:10.1109/LRA.2025.3526441,
author = {Chen, X and Chen, W and Lee, D and Ge, Y and Rojas, N and Kormushev, P},
doi = {10.1109/LRA.2025.3526441},
journal = {IEEE Robotics and Automation Letters},
pages = {2048--2055},
title = {A Backbone for Long-Horizon Robot Task Understanding},
url = {http://dx.doi.org/10.1109/LRA.2025.3526441},
volume = {10},
year = {2025}
}
TY - JOUR
AB - End-to-end robotlearning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these challenges, we propose a novel Therblig-Based Backbone Framework (TBBF) as a fundamental structure to enhance interpretability, data efficiency, and generalization in robotic systems. TBBF utilizes expert demonstrations to enable therblig-level task decomposition, facilitate efficient action-object mapping, and generate adaptive trajectories for new scenarios. The approach consists of two stages: offline training and online testing. During the offline training stage, we developed the Meta-RGate SynerFusion (MGSF) network for accurate therblig segmentation across various tasks. In the online testing stage, after a one-shot demonstration of a new task is collected, our MGSF network extracts high-level knowledge, which is then encoded into the image using Action Registration (ActionREG). Additionally, Large Language Model (LLM)-Alignment Policy for Visual Correction (LAP-VC) is employed to ensure precise action registration, facilitating trajectory transfer in novel robot scenarios. Experimental results validate these methods, achieving 94.37% recall in therblig segmentation and success rates of 94.4% and 80% in real-world online robot testing for simple and complex scenarios, respectively.
AU - Chen,X
AU - Chen,W
AU - Lee,D
AU - Ge,Y
AU - Rojas,N
AU - Kormushev,P
DO - 10.1109/LRA.2025.3526441
EP - 2055
PY - 2025///
SP - 2048
TI - A Backbone for Long-Horizon Robot Task Understanding
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2025.3526441
VL - 10
ER -
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