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This paper surveys the emerging area of Vision-Tactile-Language (VTL) models for embodied intelligence. It traces the trajectory from vision-centric to multisensory systems (Section 2), reviews tactile sensing technologies, datasets, and representations (Section 3), details approaches for vision–tactile integration (alignment, cross-attention, geometry/contact fusion; Section 4), and surveys tri-modal architectures, training strategies, and nascent evaluation for VTL (Section 5). It then covers applications in manipulation, sim-to-real, and interactive learning (Section 6), outlines challenges (data scarcity and heterogeneity, cross-modal alignment, embodiment generalization, benchmarking, efficiency, and ethics; Section 7), and offers a future outlook toward tactile foundation models, generative touch, integration with 3D physics, and VTLA/OmniVTLA systems (Section 8). The contribution is a structured, forward-looking roadmap arguing that touch closes a key gap in grounded perception and control.
Cross-Modal Consistency: 36/50
Textual Logical Soundness: 22/30
Visual Aesthetics & Clarity: 8/20
Overall Score: 66/100
Detailed Evaluation (≤ 500 words):
1. Cross-Modal Consistency
• Major 1: No figures or tables provided; survey claims on taxonomies, architectures, and benchmarks cannot be cross‑checked or quickly grasped. Evidence: No figure/table references across Sections 1–9.
• Minor 1: Simulator vs. dataset conflation may mislead readers on resources. Evidence: “Early tactile and visuotactic datasets such as Tacto … and TouchSim …” (Sec. 3.2)
• Minor 2: Unstandardized nickname “FuSe” not reflected in citation can confuse mapping to reference. Evidence: “FuSe ("Beyond Sight") fine‑tunes pretrained policies …” (Sec. 6.2)
2. Text Logic
• Major 1: Broken sentence in core architecture description blocks understanding. Evidence: “models such as tactile, and language embeddings using triplet …” (Sec. 5.2)
• Major 2: Orphaned/fragmented citation creates incomplete statement in historical context. Evidence: “(Yuan et al., 2023; Gao et al.,” (Sec. 2)
• Minor 1: Octopi claim lacks detail/context for the asserted dataset release. Evidence: “Octopi … releases the PhysiCLEAR dataset …” (Sec. 5.5)
• Minor 2: Tacto/TouchSim miscast as datasets (they are simulators), better framed as data sources. Evidence: “Early tactile and visuotactic datasets such as Tacto … and TouchSim …” (Sec. 3.2)
3. Figure Quality
• Major 1: Absence of taxonomy diagrams, model schematics, and summary tables significantly reduces clarity for a survey. Evidence: No figures or tables included.
• Minor 1: N/A
Key strengths:
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Actionable recommendations:
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This paper presents a comprehensive survey of the emerging field of Vision-Tactile-Language (VTL) models, aiming to provide a roadmap for integrating tactile sensing into multimodal AI systems. The authors begin by establishing a historical context, tracing the evolution from vision-centric AI to the inclusion of tactile information, and finally to the integration of language. They then delve into the foundational aspects of tactile sensing, discussing various sensor technologies and the challenges associated with tactile data representation and acquisition. The core of the paper focuses on the integration of vision and touch, exploring different architectural approaches and alignment strategies. The authors also highlight the emerging architectures that incorporate language alongside vision and touch, and discuss applications in embodied robotics. The paper concludes with a discussion of current challenges and future research directions, particularly emphasizing the need for tactile foundation models. Overall, the paper provides a valuable overview of a nascent field, synthesizing recent research and identifying key areas for future development. However, it also reveals some limitations, particularly in its depth of technical analysis and its engagement with the broader robotics community.
I found the paper to be a well-structured and timely survey of the rapidly evolving field of Vision-Tactile-Language models. The authors have successfully synthesized a large amount of recent research, providing a clear and accessible overview of the field's current state. The paper's organization is logical, starting with a historical context and progressing through foundational aspects of tactile sensing, integration methods, and future directions. I particularly appreciated the authors' efforts to trace the historical trajectory from vision-centric systems to the inclusion of tactile information, which effectively contextualizes the importance of this research. The discussion of tactile sensing fundamentals, including various sensor technologies and data acquisition methods, was informative and provided a solid foundation for understanding the challenges and opportunities in this area. Furthermore, the paper effectively highlights the growing interest in VTL models and the potential for tactile information to enhance embodied AI systems. The authors also did a good job of identifying key challenges, such as data scarcity, sensor heterogeneity, and the need for standardized benchmarks, which are crucial for guiding future research. The paper's forward-looking perspective, particularly the discussion of tactile foundation models, is a valuable contribution to the field. The authors have clearly identified a gap in the literature and have provided a useful resource for researchers interested in this area.
While the paper provides a valuable overview of the field, I have identified several weaknesses that warrant attention. First, the paper's contribution as a survey is somewhat limited by its narrow focus on vision, tactile, and language modalities. As noted by one reviewer, the absence of discussion regarding other critical sensors commonly used in robotics, such as audio, proprioception, and exteroception, creates a somewhat myopic view of embodied AI. The paper's title, "TOUCH BEYOND VISION," and its consistent focus on the VTL triad, while reflecting the paper's core focus, might be perceived as overstating the case if the survey is indeed broader. This narrow focus is further reinforced by the lack of discussion on how the VTL model integrates with other sensors, which are essential for a complete embodied AI system. This limitation is evident in the lack of discussion of works that incorporate these other modalities, such as the 'Assembly Instruction with Multi-modal Guidance' paper, which includes audio and proprioception. The paper also lacks a detailed technical analysis of the methods it surveys. While the authors categorize different approaches, they do not delve deeply into the specific algorithms, mathematical formulations, or architectural nuances of the cited works. For example, the paper mentions 'Feature-level alignment' using contrastive learning but does not detail the specific loss functions or network architectures used in the cited works. This lack of technical depth makes it difficult to assess the true novelty and effectiveness of the presented approaches. Furthermore, the paper does not provide a comprehensive analysis of the limitations of existing methods. While the authors do discuss some challenges, such as data scarcity and sensor heterogeneity, they do not delve into the specific shortcomings of individual techniques or architectures. For instance, the paper does not discuss the limitations of using contrastive learning for tactile representation or the challenges of aligning visual and tactile features due to their different spatial and temporal resolutions. This lack of critical analysis limits the paper's ability to guide future research effectively. The paper also suffers from a lack of quantitative evaluation. While the authors mention benchmarks and evaluation tasks, they do not present any original experimental results or comparative analyses. This absence of quantitative data makes it difficult to assess the performance of the methods discussed and to understand their strengths and weaknesses. The paper also lacks a thorough discussion of the practical implications of VTL models in robotics. While the authors mention robotic manipulation as an application, they do not provide specific examples of how these models can be used to solve real-world robotic tasks. This lack of practical examples limits the paper's relevance to the robotics community. Finally, the paper's writing style is somewhat descriptive and lacks the critical analysis expected in a high-quality survey. The paper primarily summarizes existing work without offering novel insights or perspectives. The language used is often vague and imprecise, making it difficult to understand the core contributions of the paper. For example, the paper uses terms like 'emerging architectures' and 'comprehensive survey' without clearly defining what these terms mean or providing specific examples to support these claims. This lack of clarity and critical analysis makes the paper feel more like a collection of summaries rather than a cohesive and insightful review of the field. The paper also does not adequately address the issue of tactile sensor noise and its impact on the performance of VTL models. While the paper mentions sensor heterogeneity as a challenge, it does not delve into the specific techniques used to mitigate sensor noise. This omission is significant, as tactile sensors are known to be noisy and unreliable, which can severely impact the performance of VTL models. The paper also does not discuss the limitations of current tactile sensors in terms of their spatial resolution, sensitivity, and durability. This lack of discussion limits the paper's ability to provide a realistic assessment of the current state of the field. The paper also does not adequately address the issue of sim-to-real transfer for tactile data. While the paper mentions the use of simulation tools, it does not discuss the challenges of bridging the gap between simulated and real-world tactile data. This omission is significant, as tactile data is inherently high-dimensional and noisy, which makes it difficult to obtain large amounts of real-world tactile data. Finally, the paper does not adequately address the issue of tactile perception in dynamic environments. While the paper touches upon the temporal aspects of tactile data, it does not delve into the specific challenges of processing and interpreting tactile data in dynamic environments. This omission is significant, as many real-world robotic tasks involve dynamic interactions with the environment, which require the ability to process and interpret tactile data in real-time. The paper also does not discuss the limitations of current VTL models in terms of their ability to generalize to new environments and tasks. This lack of discussion limits the paper's ability to provide a realistic assessment of the current state of the field.
To address the identified weaknesses, I recommend several concrete improvements. First, the authors should broaden the scope of the survey to include a more comprehensive discussion of other relevant modalities, such as audio, proprioception, and exteroception. This would provide a more complete picture of the current state of embodied AI and would help to contextualize the role of tactile information within a broader sensory framework. The authors should also delve deeper into the technical details of the methods they survey. This would involve providing more specific information about the algorithms, mathematical formulations, and architectural nuances of the cited works. For example, when discussing feature alignment, the authors should specify the types of loss functions used, the dimensionality of the feature embeddings, and the specific network architectures employed. Furthermore, the authors should provide a more critical analysis of the limitations of existing methods. This would involve discussing the specific shortcomings of individual techniques and architectures, as well as the challenges of aligning visual and tactile features. The authors should also include a more detailed discussion of the practical implications of VTL models in robotics. This would involve providing specific examples of how these models can be used to solve real-world robotic tasks, such as object manipulation, grasping, and navigation. The authors should also include a comparative analysis of different approaches, highlighting their strengths and weaknesses in various application scenarios. To address the lack of quantitative evaluation, the authors should consider conducting their own experiments or providing a more detailed analysis of the results presented in the cited works. This would involve comparing the performance of different methods on standardized benchmarks and discussing the factors that contribute to their performance. The authors should also address the issue of tactile sensor noise and its impact on the performance of VTL models. This would involve discussing the specific techniques used to mitigate sensor noise, such as filtering, calibration, and sensor fusion. The authors should also discuss the limitations of current tactile sensors in terms of their spatial resolution, sensitivity, and durability. To address the issue of sim-to-real transfer for tactile data, the authors should discuss the challenges of bridging the gap between simulated and real-world tactile data. This would involve discussing the techniques used to generate realistic tactile data, such as physics-based simulation and data augmentation. The authors should also discuss the limitations of these techniques and the challenges of validating the performance of VTL models in real-world scenarios. To address the issue of tactile perception in dynamic environments, the authors should discuss the specific challenges of processing and interpreting tactile data in dynamic environments. This would involve discussing the techniques used to handle the temporal aspects of tactile data, such as time-series analysis and recurrent neural networks. The authors should also discuss the limitations of these techniques and the challenges of developing VTL models that can operate in real-time. Finally, the authors should improve the overall clarity and organization of the paper. This would involve using more precise language, providing clear definitions of key terms, and structuring the paper in a more logical and coherent manner. The authors should also ensure that the paper is accessible to a broad audience, including researchers from different fields. The authors should also consider releasing their collected dataset to the community, which would be a valuable contribution to the field. This dataset could be used by other researchers to train and evaluate their own models, and it would help to accelerate the progress of research in this area.
I have several questions that arise from my analysis of the paper. First, given the paper's focus on vision, tactile, and language modalities, I am curious about the authors' rationale for excluding other critical sensors commonly used in robotics, such as audio, proprioception, and exteroception. What specific challenges or limitations led to this narrow focus, and how might the inclusion of these other modalities impact the development of VTL models? Second, I am interested in the authors' perspective on the specific limitations of current tactile sensors, particularly in terms of their spatial resolution, sensitivity, and durability. How do these limitations affect the performance of VTL models, and what are the potential solutions for overcoming these challenges? Third, I would like to know more about the authors' thoughts on the challenges of sim-to-real transfer for tactile data. What specific techniques have been used to generate realistic tactile data, and what are the limitations of these techniques? How can we validate the performance of VTL models in real-world scenarios, given the difficulty of obtaining large amounts of real-world tactile data? Fourth, I am curious about the authors' perspective on the limitations of current VTL models in terms of their ability to generalize to new environments and tasks. What are the specific challenges of developing VTL models that can operate in diverse and dynamic environments, and what are the potential solutions for addressing these challenges? Finally, I would like to know more about the authors' plans for future research in this area. What specific research questions do they intend to address, and what are their expectations for the future development of VTL models?