Mobile Manipulation Instruction Generation from Multiple Images with Automatic Metric Enhancement (2501.17022v1)

Abstract: We consider the problem of generating free-form mobile manipulation instructions based on a target object image and receptacle image. Conventional image captioning models are not able to generate appropriate instructions because their architectures are typically optimized for single-image. In this study, we propose a model that handles both the target object and receptacle to generate free-form instruction sentences for mobile manipulation tasks. Moreover, we introduce a novel training method that effectively incorporates the scores from both learning-based and n-gram based automatic evaluation metrics as rewards. This method enables the model to learn the co-occurrence relationships between words and appropriate paraphrases. Results demonstrate that our proposed method outperforms baseline methods including representative multimodal LLMs on standard automatic evaluation metrics. Moreover, physical experiments reveal that using our method to augment data on language instructions improves the performance of an existing multimodal language understanding model for mobile manipulation.

• The paper introduces a dual-image based instruction generation model that integrates target and receptacle visuals via a novel Triplet Qformer.
• It employs a Human Centric Calibration Phase combining learning and n-gram metrics, significantly enhancing linguistic diversity and coherence.
• Experimental results on the HM3D-FC dataset demonstrate that the method outperforms state-of-the-art models across key metrics like CIDEr, SPICE, and BLEU4.

Overview of Mobile Manipulation Instruction Generation from Multiple Images with Automatic Metric Enhancement

The paper discusses a novel approach to generating free-form instructions for mobile robot manipulation using simultaneous input from two images: one of a target object and the other of a receptacle. The proposed method addresses limitations in existing image captioning models, which typically process single images and therefore are inadequate for tasks requiring integration of multimodal input. By enhancing the generation of mobile manipulation instructions, robots can more effectively perform tasks in service environments such as elderly care facilities.

Key Contributions

1. Model Architecture:
   • The proposed model integrates visual information from both target object and receptacle images, effectively handling the task of generating coherent manipulation instructions.
   • A novel component, Triplet Qformer, is introduced for aligning visual features of the target and receptacle images with text features from instruction sentences.
   • The model architecture utilizes multiple visual encoding strategies, employing both local (region features) and global (grid features) information to overcome the limitations of existing image captioning methods.
2. Training Methodology:
   • A novel training approach, Human Centric Calibration Phase (HCCP), is outlined, leveraging a combination of learning-based and $n$-gram-based automatic evaluation metrics. This enables the model to balance between capturing co-occurrence relationships of words while maintaining appropriate paraphrasing in generated instructions.
   • The inclusion of $n$-gram and learning-based metrics in loss computation improves the ability to handle diverse linguistic outputs, facilitating enhanced performance compared to baseline methods.
3. Experimental Validation:
   • The effectiveness of the proposed method is validated using standard evaluation metrics such as Polos, PAC-S, RefPAC-S, SPICE, CIDEr, and BLEU4.
   • The method consistently outperformed existing state-of-the-art multimodal LLMs and image captioning techniques across all metrics on the HM3D-FC dataset.
4. Data Augmentation and Practical Implications:
   • By generating high-quality instruction sentences, the method facilitates data augmentation that improves the performance of existing multimodal language understanding models.
   • In both simulated and physical environments, augmented datasets improved the identification and retrieval of relevant objects, highlighting the practical utility of the proposed methodology.

Critical Evaluation and Future Directions

The method demonstrates significant advancement in handling multimodal inputs for generating robot manipulation instructions. By addressing challenges in aligning image features with linguistic outputs, it achieves notable improvements in both automated metrics and real-world applicability. Moreover, the introduction of the Triplet Qformer provides a robust mechanism for visual-text alignment that is crucial to grounding instructions in contextual visual data.

While successful, attention to future exploration areas might include tackling the remaining issues with color description inaccuracies—potentially integrating more sophisticated color detection modules into the framework. Further research could also explore generalization to more complex and dynamic environments, extending beyond the current scope of static image pairs.

In summary, this approach marks a pivotal contribution to the field of robotic vision and language processing, offering a scalable solution for generating precise and contextually-aware manipulation instructions, essential for advancing autonomous service robots.