AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

Authors: Jiafei Duan, Wilbert Pumacay, Nishanth Kumar, Yi Ru Wang, Shulin Tian, Wentao Yuan, Ranjay Krishna, Dieter Fox, Ajay Mandlekar, Yijie Guo
Primary area: applications to robotics, autonomy, planning
Keywords: robotic manipulation; data generation; vision-language-model; failure reasoning; failure detection

Abstract:
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models (VLMs) and large language models (LLMs) have improved robots' spatial reasoning and problem-solving abilities, they still struggle with failure recognition, limiting their real-world applicability. We introduce AHA, an open-source VLM designed to detect and reason about failures in robotic manipulation using natural language. By framing failure detection as a free-form reasoning task, AHA identifies failures and provides detailed, adaptable explanations across different robots, tasks, and environments. We fine-tuned AHA using FailGen, a scalable framework that generates the first large-scale dataset of robotic failure trajectories, the AHA dataset. FailGen achieves this by procedurally perturbing successful demonstrations from simulation. Despite being trained solely on the AHA dataset, AHA generalizes effectively to real-world failure datasets, robotic systems, and unseen tasks. It surpasses the second-best model (GPT-4o in-context learning) by 10.3% and exceeds the average performance of six compared models including five state-of-the-art VLMs by 35.3% across multiple metrics and datasets. We integrate AHA into three manipulation frameworks that utilize LLMs/VLMs for reinforcement learning, task and motion planning, and zero-shot trajectory generation. AHA’s failure feedback enhances these policies' performances by refining dense reward functions, optimizing task planning, and improving sub-task verification, boosting task success rates by an average of 21.4% across all three tasks compared to GPT-4 models. Project page: https://aha-vlm.github.io

Decision: accept (poster)
Reviewer scores: 6, 5, 6, 8

Reviews and discussion:
<thread>
creation date:
1729139725033

thread_type:
official_review

<thread_object>
creator: reviewer_qhqf

<content>
summary:
The paper presents a Vision-Language Model (VLM) for robotic failure reasoning, introducing a failure dataset and fine-tuning the VLM to achieve this goal. The approach demonstrates promising performance.

strengths:
Failure trajectory generation can be extended to other scenarios (simulatores), encompassing a comprehensive set of failure categories. Failure reasoning plays a critical role in downstream policy learning, which is essential for advancing the entire community

weaknesses:
Baseline Comparisons:
 - Clarity of Baseline Implementation: The implementation details of the baselines are unclear. What type of input data are the baselines using? Is it a single image, a sequence of images, images from multiple views, or video? This distinction significantly affects baseline performance. For instance, Gemini excels at reasoning for robotic manipulation when using video inputs, but its performance would likely degrade if multiple-view images were used instead.

 - Lack of Comparison with Robotic-Specific Methods: Why not compare your approach with other robotic-oriented reasoning methods mentioned in the 'Introduction' or 'Related Work' sections? The current experiments only compare against general Vision-Language Models (VLMs), which are not specifically designed for robotic tasks.

I would consider raising my score if these issues are addressed or clarified. Otherwise, it's difficult to properly assess how well the model performs.

questions:
- View Angle Concerns: The camera view angles during training are fixed according to the RLBench settings. This raises concerns about the view angles during testing. Specifically: 1) How many views does the model require during testing? 2) Should the view angles during testing match the exact poses used in the RLBench training setting?

- Missing Reference to Figure 1: Figure 1 is not referenced or discussed anywhere in the paper, which needs to be addressed for clarity and completeness.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730463582864

thread_type:
official_review

<thread_object>
creator: reviewer_fdia

<content>
summary:
This work proposes a VLM for detecting and reasoning about failures in robotic manipulation, which can be applied in various downstream tasks.

strengths:
1. The motivation is meaningful, and I agree that addressing failures in robotics is important.
2. It can be utilized in downstream tasks.
3. The website has many great demos.

weaknesses:
I think the types of failure modes and the reasoning responses are somewhat rough. And more details and experiments need to be conducted. For further details, please refer to the "Questions" section.

questions:
1.What is the difference between rotation and no rotation?
2.What are the details of your baseline settings? I couldn't find them in your paper.
3.Are you using the REFLECT framework for your baseline? If not, I believe REFLECT is highly relevant to your work, so I would like to see a comparison of its performance with your model.
4.How do you plan to extend your failure types? As of now, I still find them somewhat rough.

My main concern is the sufficiency of the experiments and the work still seems somewhat preliminary. However, if you address my concerns, I will be inclined to raise my score.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730615382628

thread_type:
official_review

<thread_object>
creator: reviewer_enxm

<content>
summary:
This paper introduces AHA, a new VLM model to identify and describe failure modes in robot manipulation. The authors introduce a pipeline called FailGen to generate failure demonstrations and use the data to train AHA. In the experiments, AHA demonstrates improved performance in failure reasoning. The authors also show its effectiveness in downstream application including VLM-based reward design, planning, and subtask verification.

strengths:
1. The idea of using failure detection VLM to improve VLM-based robotic application is interesting.
2. The effort in developing a failure dataset is valuable towards building robust large models that can understand the physical world.

weaknesses:
1. [Soundness] It remains unclear to me whether incorporating failure descriptions is a reliable approach to improve VLM in reward design, planning, and subtask verification. For example, in the VLM-based reward design, although VLM can provide some penalty reward terms based on AHA input to correct behavior, there is no guarantee the new reward can make robot perform better than the previous one (e.g. the penalty can be too large and hinder skill learning). In this case, I would like to see if the proposed method can show some iterative reward tuning behavior based on failure feedback and the procedure can converge (i.e., tuning the strength of new terms or number of terms to correct behavior). 

2. [Clarity] The paper can be much clearer and convincing if the authors can show more extensive comparisons between AHA and GPT4 to understand what leads to the improvements. For example, is the improvement due to GPT4 not being able to generate accurate failure description? Or, is it simply because the generated failure text is too lengthy for the downstream VLM model to understand? I encourage more explicit discussion on what a good failure description looks like (for downstream task VLMs). I also suggest comparing to human oracles with different writing styles.

3. [Result: Generalization] The paper is focusing on simple pick-and-place style tasks in clean backgrounds. How about more challenging and dexterous tasks? I would like to see the results on more challenging task such as guiding a robot hand to perform grasping and reorientations where the failure modes can be more fine-grained and complex. Besides, explicitly discussing generalization to diverse backgrounds / objects is also necessary. The results on these harder setups can help readers to understand the opportunities and challenges of AHA in application.

questions:
See the weaknesses part. The idea is interesting and I would like to raise my score if the authors can show some rigorous results to clarify the points above, especially 1 and 2. I also encourage discussion on negative results to help readers see the limitation and potential of the current version.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730639064315

thread_type:
official_review

<thread_object>
creator: reviewer_zg7s

<content>
summary:
This work targets the task of open-world failure detection and reasoning under the scenario of robotic manipulation, which is critical and valuable for diverse downstream tasks. Specifically, the authors propose a procedural pipeline for generating failure demonstration data for robotic manipulation in simulation, upon which they fine-tune a vision-language model (VLM) for failure detection and reasoning. Moreover, they demonstrate improved performance brought by the fine-tuned VLM in three downstream tasks, namely reward function design, task-plan refinement, and sub-task verification. Comprehensive comparison with the other six VLMs also shows the superior performance of the proposed technique on this task.

strengths:
- The paper tackles an important problem that is highly beneficial and safety-critical when learning-based robotic algorithms are deployed in the real world;

- The developed procedural pipeline of simulation data generation is useful for researchers in the community who are willing to contribute to this direction;

- It is inspiring to see that not only improved performance is shown on the standalone task, but to see the strong benefits brought by such improvement on different related and popular downstream tasks.

- Though failure detection is an important prerequisite for the reasoning part, I am still glad to see the efforts in investigating the failure reasoning and especially its usage in the downstream tasks.

- The presentation is generally well-structured and easy to follow.

- Though benchmarking experiments are conducted in the simulation, their results are comprehensive and informative.

weaknesses:
Presentation:
- It would be clearer if subsection 4.1. can be shortened a bit and described with a more concise mathematical formulation. 

- To reinforce the contribution, I would suggest adding a subsection in the method section to explain how improved failure detection and reasoning performance can help these downstream tasks. In the current draft, putting this part in the experiment section is less apparent and inspiring for potential readers in these fields. 

- Closely related to the point above, it seems that the usage of the proposed idea in downstream task 2 (refine task plans in TAMP) and task 3 (improve task verification) is essentially similar, i.e., detect and reason for the failure of the sub-tasks. It might be more lucid for the readers to group these two tasks together along with the reward design task and make a taxonomy for different high-level manners of how failure detection and reasoning can help. 

- in sub-section 4.1., "Unlike previous works that primarily focus on detecting task success as binary classification problem, we approach failure reasoning by first predicting a binary success condition ("Yes" or "No") of the given sub-task based on a language specification and an input image prompt." seems less straightforward to understand. Logically, I suspect the author wants to express that they have an additional reasoning part after the detection step. 


- Fig.1 and Fig.2 are hard to read. It would be better to reorganize the sub-figures and increase the sizes of the images and texts.

Methodology:

- The first point I would like to raise is the lack of discussion of hallucination in LLM/VLMs. In the introduction, the authors motivate the failure recognition with the shortcoming of hallucinations in LLMs/VLMs. However, the proposed fine-tuned VLM will still suffer from this issue. It would be important to discuss how to mitigate this issue in detecting failures. In this regard, there is a missing citation [1] that is closely related to this work, which made an initial attempt in this direction based on the uncertainty derived from the VLM. I would suggest the authors add a discussion on this aspect. It would also be meaningful to see the influence of fine-tuning with the proposed data set on the performance of uncertainty estimation. Will there be a trade-off between the performance of the prediction accuracy and uncertainty estimation?  

- The second point is related to the design of the technique. In [1], it has been observed that using the "chain-of-thoughts" (CoT) analysis in the prompt template can improve the performance of failure detection. Therefore, related to the reasoning part of this work, will the reasoning part be able to help improve the performance failure detection? Will the performance be influenced by the different prompting templates, e.g., with and w/o CoT?

Experiments:

- Due to missing real-world experiments, therefore, it is hard to evaluate the performance loss during sim-to-real transfer and the actual practicality of the proposed technique. 
 

[1] "Zheng, Zhi, et al. "Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners." arXiv preprint arXiv:2406.00430 (2024)."

questions:
Side questions (major ones in the Weaknesses section above)

- In subsection 3.1., why separate the rotation failure into incorrect and missing ones? Should these two belong to the same category?  

- An ablation study on how many camera viewpoints would be insightful for the real-robot applicability of the proposed method.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732069263779

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Rebuttal to All Reviewers

comment:
We are grateful to the reviewers for their thorough efforts in reviewing the paper and providing us with thoughtful feedback, which has been invaluable in improving this work.

We appreciate the positive feedback highlighting that the paper is "well-motivated" and addresses failure reasoning, an "important" problem for the robotics community [zG7s, FdiA, qHQF]. We are grateful that reviewers found our problem formulation, writing, and demos to be clear, well-structured, and easy to follow [zG7S, FdiA]. The results were described as impressive, comprehensive, and informative [zG7S], with reviewers recognizing the novelty of our proposed approach [Enxm]. Additionally, many reviewers acknowledged the effectiveness of AHA in improving downstream robotics applications [Enxm, FdiA, zG7s].

Despite the positive feedback, reviewers have highlighted several areas for improvement, which we have summarized below:

1. **Clarity in Writing for Downstream Tasks** [zG7S, Enxm, FdiA,qHQF]
   - Reviewers emphasized the need for clearer articulation of how our approach supports downstream robotics tasks.
2. **Correlation Between AHA’s Performance and Prediction’s Uncertainty Estimation** [zG7S]
   - There is interest in further exploring the relationship between AHA’s model performance and its prediction uncertainty estimation.
3. **Comparison With Conventional Approaches** [FdiA, qHQF]
   - A deeper comparison between AHA and traditional failure reasoning methods, such as those proposed in REFLECT, was requested.
4. **Impact of Instruction-tuning Across Viewpoints** [qHQF, zG7S]
   - Reviewers noted the need for additional analysis on how instruction-finetuning with our proposed datasets affects performance when evaluated with varying numbers of viewpoints.
5. **Future Work on Generalization Across Domains** [Enxm, FdiA]
   - Suggestions were made to expand the discussion of future work, particularly how AHA could generalize to broader robotics domains such as locomotion, bimanual manipulation, and navigation.

In response to the reviewers' feedback, we have uploaded a revised version of the paper (revisions highlighted in blue text) with the following changes and conducted several additional experiments and ablation studies.

1. **[Improved Clarity on AHA’s Contributions](https://openreview.net/forum?id=JVkdSi7Ekg&noteId=NRk7OKWnDY)**  
   - Section 4 method section has been revised, including a new subsection to better articulate AHA’s contributions to downstream tasks.

2. **[Prediction Uncertainty Analysis](https://openreview.net/forum?id=JVkdSi7Ekg&noteId=mAj8LuN6kr)**  
   - Additional experiments have been conducted to compare AHA's average sentence output likelihood probabilities with those of LLaVA-v1.5-13B, providing new insights into AHA’s prediction uncertainty.

3. **[Comparison with REFLECT](https://openreview.net/forum?id=JVkdSi7Ekg&noteId=Zjm2CDYIbP)**  
   - REFLECT has been implemented on a subset of the RoboFail evaluation dataset to enable a direct empirical comparison with AHA.

4. **[Ablation Studies on Viewpoints](https://openreview.net/forum?id=JVkdSi7Ekg&noteId=BtK7x2GJvA)**  
   - We conducted further ablation studies to analyze the impact of varying numbers of viewpoints on AHA’s performance during evaluation.

5. **[Expanded Discussion on Limitations and Future Directions](https://openreview.net/forum?id=JVkdSi7Ekg&noteId=Zjm2CDYIbP)**  
   - Section 6 conclusion section and supplementary material have been revised to include a more detailed discussion of AHA’s limitations and potential future work, including ways to extend AHA to other domains.


**We sincerely hope that by addressing all of the reviewers' concerns and providing the necessary clarifications, our paper may be considered for an improved rating. If there are any further questions, please do not hesitate to let us know. Thank you for your time and thoughtful review.**
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper introduces AHA, a vision-language model for detecting and reasoning about failures in robotic manipulation tasks. The method demonstrates promising performance in analyzing manipulation failures and improving downstream robotic applications, receiving generally positive reviews across all reviewers (scores 5-8). Reviewers consistently praised the paper's meaningful motivation of addressing failure reasoning in robotics, its novel contribution of a failure dataset generation framework, and the demonstrated utility in multiple downstream applications including reward design, planning, and verification tasks.

Initial concerns centered on three main areas: the lack of rigorous evaluation and comparison with robotics-specific baselines like REFLECT, unclear implementation details about input modalities and baseline configurations, and questions about generalization beyond simple pick-and-place scenarios. The authors provided detailed responses, implementing new comparisons with REFLECT that showed AHA's superior performance, clarifying their standardized input protocol across baselines, and conducting additional ablation studies on viewpoint variations. They also demonstrated comparable performance to human oracle evaluations in some cases.

While two reviewers were satisfied with these responses and maintained positive scores, one reviewer remained concerned about the fundamental limitations of using failure descriptions for improving downstream tasks, noting that even with human-generated descriptions the success rates remained below 80%. However, the authors clarified that AHA serves to enhance existing frameworks rather than replace them entirely, showing consistent improvements over baselines across different applications.