@inproceedings{liu-etal-2025-vlp,
title = "{VLP}: Vision-Language Preference Learning for Embodied Manipulation",
author = "Liu, Runze and
Bai, Chenjia and
Lyu, Jiafei and
Sun, Shengjie and
Du, Yali and
Li, Xiu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1444/",
pages = "28416--28432",
ISBN = "979-8-89176-332-6",
abstract = "Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel \textbf{V}ision-\textbf{L}anguage \textbf{P}reference learning framework, named \textbf{VLP}, which learns a vision-language preference model to provide feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection."
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<abstract>Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel Vision-Language Preference learning framework, named VLP, which learns a vision-language preference model to provide feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection.</abstract>
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%0 Conference Proceedings
%T VLP: Vision-Language Preference Learning for Embodied Manipulation
%A Liu, Runze
%A Bai, Chenjia
%A Lyu, Jiafei
%A Sun, Shengjie
%A Du, Yali
%A Li, Xiu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-vlp
%X Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel Vision-Language Preference learning framework, named VLP, which learns a vision-language preference model to provide feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection.
%U https://aclanthology.org/2025.emnlp-main.1444/
%P 28416-28432
Markdown (Informal)
[VLP: Vision-Language Preference Learning for Embodied Manipulation](https://aclanthology.org/2025.emnlp-main.1444/) (Liu et al., EMNLP 2025)
ACL