Vision-Language Models as Success Detectors
Using Flamingo as a success detector framed as VQA, achieving cross-domain generalization with minimal human annotations.
Key Findings
Methodology
This work formulates success detection as a visual question answering (VQA) task, leveraging the pretrained Flamingo model. Fine-tuning involves only visual layers with human-annotated success trajectories across three domains: simulated household, robotic manipulation, and in-the-wild human videos. The model inputs scene videos/images and success questions, producing yes/no answers. This approach exploits Flamingo’s multimodal pretraining to enable zero-shot generalization to unseen tasks and visual variations, outperforming bespoke reward models in out-of-distribution tests.
Key Results
- In the simulated household environment, the fine-tuned Flamingo model achieved 59.3% success detection accuracy on unseen tasks, surpassing the 49.9% of domain-specific reward models. In robotic tasks, accuracy reached 83.4%, exceeding baseline 80.6%. In wild human videos, results are promising but still challenging, indicating strong generalization potential. The model maintains robustness under visual perturbations and task rephrasing, demonstrating the effectiveness of large-scale pretraining for success detection.
Significance
This research addresses the limitations of traditional reward models that lack flexibility across tasks and environments. By formalizing success detection as a multimodal VQA task, it provides a unified, scalable framework that leverages large pretrained models. The approach reduces annotation costs and enhances robustness, paving the way for more adaptable autonomous agents capable of learning in complex, real-world settings. It also opens avenues for integrating semantic understanding into reward modeling, crucial for advancing reinforcement learning and behavior evaluation.
Technical Contribution
Key innovations include the formalization of success detection as a VQA problem, the adaptation of Flamingo’s multimodal capabilities for success classification, and the demonstration of zero-shot generalization across diverse domains. The methodology involves minimal parameter updates, focusing on visual modules, which simplifies training. This work bridges large-scale pretraining with practical reward modeling, enabling scalable, domain-agnostic success detection that surpasses prior domain-specific reward models in out-of-distribution scenarios.
Novelty
This is the first work to directly utilize large pretrained vision-language models like Flamingo for success detection across multiple real-world and simulated domains. Unlike prior methods relying on task-specific reward engineering or contrastive models like CLIP, this approach leverages generative multimodal pretraining to achieve semantic understanding and robust generalization, especially in unseen tasks and visual conditions.
Limitations
- The model struggles with highly dynamic, multi-agent, or subtle success cues in complex real-world environments, indicating room for improvement in temporal and contextual understanding.
- Dependence on human-annotated success trajectories limits scalability; unsupervised or weakly supervised methods could further reduce annotation costs.
- Computational costs for fine-tuning large models remain high, and real-time deployment in embedded systems needs further optimization.
Future Work
Future directions include integrating self-supervised learning to reduce annotation dependency, extending success detection to multi-agent and multi-modal scenarios, and exploring online adaptation techniques. Enhancing temporal reasoning and context-awareness will improve performance in dynamic environments. Additionally, scaling to larger models and datasets may further boost generalization, bringing us closer to truly universal reward models for autonomous agents.
AI Executive Summary
This study introduces SuccessVQA, a novel framework that leverages the pretrained Flamingo vision-language model for success detection framed as a visual question answering task. By fine-tuning only the visual components with human-annotated success trajectories, the approach achieves remarkable generalization across three diverse domains: simulated household environments, robotic manipulation, and in-the-wild human videos. The core innovation lies in formalizing success detection as a multimodal VQA problem, enabling the model to understand complex scene semantics and task success criteria without domain-specific engineering.
Experimental results demonstrate that the fine-tuned Flamingo model outperforms traditional, handcrafted reward models, especially in out-of-distribution scenarios. In the simulated environment, success detection accuracy on unseen tasks reached 59.3%, surpassing the 49.9% of bespoke models. In robotic tasks, accuracy was 83.4%, exceeding baseline performance, while in wild videos, initial results show promising robustness despite inherent challenges.
This framework's significance is profound: it offers a scalable, unified solution for success detection that reduces annotation costs and enhances robustness. By harnessing large-scale pretraining, the model exhibits a semantic understanding capable of generalizing to new tasks and visual conditions, addressing a critical bottleneck in reinforcement learning and autonomous behavior evaluation.
Despite these advances, challenges remain in dynamic, multi-agent, or subtle success cues, and computational costs are high. Future work will focus on integrating self-supervised learning, expanding to more complex scenarios, and optimizing for real-time deployment. Overall, SuccessVQA paves the way for more adaptable, intelligent agents capable of learning and operating effectively in complex real-world environments.
Deep Analysis
Background
近年来，视觉-语言模型（如CLIP、ALIGN、Flamingo）在多模态理解中取得显著突破，推动了图像识别、问答和生成等任务的发展。传统成功检测方法多依赖于环境特定的奖励函数或手工规则，难以推广到复杂、多变的真实场景。随着大规模预训练模型的出现，研究者开始利用其强大的语义理解能力，构建更具泛化能力的奖励和行为评价机制。这一趋势旨在解决现有方法在多任务、多环境中的适应性不足问题，为自主智能体的普适性发展提供新思路。
Core Problem
核心问题在于如何在多样化场景和视觉条件下，准确识别行为是否成功。传统奖励模型依赖环境标注，难以泛化，且标注成本高昂。面对未见任务和视觉变化时，模型表现不佳，限制了自主学习能力。如何利用预训练模型的语义理解，实现跨任务、跨环境的成功检测，成为亟待解决的难题。
Innovation
本研究的创新点包括：1）将成功检测形式化为视觉问答（VQA）任务，统一多场景应用；2）利用预训练的Flamingo模型，结合少量人类标注，实现跨任务和视觉变化的零-shot泛化；3）只微调视觉模块，保持语言层冻结，简化训练流程。这些创新增强了模型的语义理解和场景适应能力，显著降低训练成本。
Methodology
- �� 输入：场景视频或图像，任务描述文本。• 模型架构：基于预训练的Flamingo，将视觉编码器、交叉注意力层微调，保持语言层冻结。• 训练：利用人类标注的成功轨迹，生成对应的VQA问题（如“是否成功完成任务？”），模型输出“是”或“否”。• 数据处理：将轨迹切割成固定长度片段，生成多样化的问答样本。• 微调：只调整视觉相关层，利用交叉熵损失优化模型。• 评估：在不同场景下测试模型的成功识别率，比较定制奖励模型的性能。• 跨域泛化：在未见任务和视觉变化条件下验证模型鲁棒性。
Experiments
在模拟家庭、机器人和“野外”视频三大场景中，使用人类标注的成功轨迹作为训练数据。模型在未见任务和视觉变化下进行测试，指标包括成功识别准确率和鲁棒性。对比基线包括手工设计的奖励模型和未微调的Flamingo。实验还分析了不同任务复杂度和视觉干扰对模型性能的影响，验证了模型的泛化能力和实用性。
Results
模型在模拟家庭环境中，成功识别率在未见任务上达59.3%，优于专用奖励模型的49.9%；机器人任务中，准确率达83.4%，超越基线80.6%；在“野外”视频中，表现虽有挑战，但已展现一定的泛化潜力。模型在不同视觉条件下表现稳定，验证了预训练模型的优势。整体结果表明，SuccessVQA框架具有强大的跨任务和视觉变化适应能力。
Applications
该方法可广泛应用于机器人、虚拟助手和自主系统的行为评估，尤其在多任务、多环境的复杂场景中。只需少量标注，即可实现高效的行为成功检测，为自主学习和强化学习提供可靠的奖励信号。未来还可结合在线学习和自监督技术，进一步提升模型的适应性和智能水平。
Limitations & Outlook
当前模型在极端复杂或动态场景中的表现仍有限，尤其在多主体交互和微妙行为识别方面存在不足。模型对大规模标注数据依赖较大，且在“野外”环境中泛化能力仍需提升。未来需优化模型结构，减少对标注的依赖，并增强对长时序和动态场景的理解能力。
Plain Language Accessible to non-experts
想象你在一个工厂里，有很多不同的机器在工作。每台机器都要完成特定的任务，比如装配、搬运或检测。现在，工厂想要一个智能机器人，能自动判断每台机器是否成功完成了任务。传统的方法需要人为设定规则，比如“如果装配完毕，就算成功”，但这个规则只适用于特定场景，不能应对变化。于是，科学家们用了一种叫“视觉问答”的方法，就像让机器人看着机器的工作状态，然后问它“成功了吗？”机器人用预先学会理解图像和语言的模型，来回答“是”或“否”。这个模型经过特别训练后，不仅能在工厂里识别成功，还能在不同的工厂、不同的机器和不同的环境中工作。它就像一个聪明的工厂助手，能适应各种变化，帮助工厂更高效地运转。
ELI14 Explained like you're 14
想象你在玩一个游戏，你的任务是让机器人帮你完成一些事情，比如把玩具放到指定的位置。每次你让机器人做完后，你会告诉它“做得好”或者“还需要改进”。但是，怎么让机器人知道自己做得对不对呢？以前的方法是给它写很多规则，比如“如果玩具在正确的位置，就算成功”。可是，这样的方法很麻烦，也不能应对所有情况。现在，科学家们用了一种特别聪明的机器人，它可以“看”到场景，然后“问”自己“我成功了吗？”这个问题就像你问朋友“我做得对不对？”机器人用一种叫“视觉问答”的方法，结合它之前学到的知识，自动判断自己是否成功。这样，无论场景变得多复杂，它都能自己判断，变得更聪明、更灵活。这就像你有一个超级聪明的朋友，总能帮你判断事情是不是做得好！
Abstract
Detecting successful behaviour is crucial for training intelligent agents. As such, generalisable reward models are a prerequisite for agents that can learn to generalise their behaviour. In this work we focus on developing robust success detectors that leverage large, pretrained vision-language models (Flamingo, Alayrac et al. (2022)) and human reward annotations. Concretely, we treat success detection as a visual question answering (VQA) problem, denoted SuccessVQA. We study success detection across three vastly different domains: (i) interactive language-conditioned agents in a simulated household, (ii) real world robotic manipulation, and (iii) "in-the-wild" human egocentric videos. We investigate the generalisation properties of a Flamingo-based success detection model across unseen language and visual changes in the first two domains, and find that the proposed method is able to outperform bespoke reward models in out-of-distribution test scenarios with either variation. In the last domain of "in-the-wild" human videos, we show that success detection on unseen real videos presents an even more challenging generalisation task warranting future work. We hope our initial results encourage further work in real world success detection and reward modelling.