Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models
MoSS framework integrates tactile and torque signals into VLAs, boosting success rates to 49.0% in contact-rich tasks.
Key Findings
Methodology
MoSS extends pretrained VLAs with decoupled multimodal streams processed via joint cross-modal self-attention. The approach employs a two-stage training scheme: first freezing the VLA backbone and training only the new modality streams; then unfreezing all parameters for end-to-end fine-tuning. An auxiliary future signal prediction task is incorporated to model contact dynamics. The core algorithm utilizes diffusion-based generative models, combining multimodal cues for robust action prediction. This architecture enables effective, scalable integration of heterogeneous physical signals, enhancing manipulation performance.
Key Results
- In four real-world contact-rich tasks, MoSS outperforms baseline models, achieving an average success rate of 49.0%, over 20% higher than single-modality models. For example, success in ‘Unstack Cup’ increased from 20.8% to 54.2%, and ‘Board Erase’ from 20.8% to 41.7%. Combining tactile and torque signals yields the most significant improvements, demonstrating effective multimodal synergy.
- Across different hardware setups, including dexterous hands and robotic arms, MoSS effectively integrates diverse sensors (e.g., fingertip pressure, joint torque), with only a 2.4 ms increase in inference latency, confirming real-time applicability.
- Ablation studies reveal that decoupled streams, two-stage training, and future signal prediction are critical for performance gains. The architecture’s robustness is validated through extensive experiments, showing consistent improvements across tasks and modalities.
Significance
This work addresses the key limitation of vision-only models in contact-rich manipulation, pioneering a unified framework for multimodal physical feedback integration. It significantly advances the development of generalist robots capable of dexterous, contact-aware manipulation in complex environments. The approach sets a new standard for multimodal sensor fusion, with broad implications for industrial automation, service robotics, and autonomous systems, enabling robots to operate more like humans by leveraging multiple sensory cues for precise control.
Technical Contribution
The paper introduces a decoupled multimodal stream architecture with joint cross-modal self-attention, avoiding interference with pretrained features. The two-stage training scheme preserves prior knowledge while enabling effective multimodal learning. Incorporating diffusion models for action generation and auxiliary future signal prediction enhances dynamic understanding and robustness. These innovations collectively push the frontier of multimodal robotic control, offering scalable, efficient, and versatile solutions.
Novelty
This is the first work to systematically integrate multiple physical sensory signals—touch, force, torque—into pretrained VLAs within a unified framework. The decoupled stream design and two-stage training strategy ensure stable, effective fusion, addressing limitations of prior single-modality or ad hoc multi-modality approaches. The application of diffusion models for multimodal action synthesis further distinguishes this work, setting a new benchmark in contact-rich manipulation.
Limitations
- Sensor durability and noise remain challenges for long-term deployment, requiring hardware improvements and calibration strategies.
- The model’s reliance on initial contact or grasping success limits correction capabilities during early phases, necessitating integration with more robust visual perception and planning modules.
- Additional computational overhead from multimodal processing, though minimal, could impact real-time performance in highly complex scenarios. Future work should optimize efficiency and robustness.
Future Work
Future directions include expanding to more sensory modalities (e.g., acoustic, thermal), enhancing self-supervised learning for better generalization, and deploying in more diverse, unstructured environments. Combining multimodal feedback with reinforcement learning could further improve autonomous adaptation. Additionally, hardware improvements for sensor robustness and scalable training techniques will be critical to translate these advances into real-world industrial and service robots.
AI Executive Summary
Humans excel at manipulating objects by integrating multiple senses—vision, touch, force—allowing for precise, adaptive control. Traditional robotic systems, however, rely heavily on visual cues, limiting their performance in contact-rich tasks. This gap has hindered robots from achieving human-like dexterity in complex environments.
Recent advances in vision-language models (VLAs) have significantly improved robotic perception and planning, but their reliance on visual data alone remains a bottleneck. To address this, the authors introduce MoSS, a novel framework that enhances VLAs by incorporating multiple physical sensory signals, specifically tactile and torque feedback. The core idea is to process these signals through dedicated, decoupled streams that interact via joint cross-modal self-attention, enabling the model to reason about contact dynamics effectively.
The technical innovation lies in a two-stage training scheme. Initially, the physical modality streams are trained while the pretrained VLA backbone remains frozen, ensuring stable feature alignment. Subsequently, the entire model is fine-tuned end-to-end, with an auxiliary task predicting future physical signals to better model contact interactions. This approach leverages diffusion-based generative models to produce accurate, robust actions.
Experimental results on four real-world contact-rich tasks demonstrate that MoSS achieves an average success rate of 49.0%, outperforming existing methods by over 20%. Notably, the model maintains low inference latency, only increasing by 2.4 milliseconds when integrating multiple signals, confirming its practicality for real-time applications. Ablation studies highlight the importance of the decoupled architecture, two-stage training, and future signal prediction.
This work marks a significant step toward generalist robots capable of nuanced, contact-aware manipulation. It opens avenues for integrating richer sensory feedback, improving robustness, and deploying in unstructured environments. Limitations include sensor durability and computational overhead, which future research aims to mitigate, pushing robotics closer to human-like dexterity.
Deep Analysis
Background
机器人控制技术近年来经历了深度学习和预训练模型的快速发展，代表性工作包括CLIP、DINO等在视觉和语言理解中的突破。早期模型多依赖单一模态，难以应对复杂环境中的多感官交互。随着多模态感知硬件的普及，研究逐渐转向融合触觉、力觉等信息，以提升机器人在细粒度操控中的表现。现有方法如Tactile-VLA、ForceVLA等，虽实现一定程度的多模态融合，但多为单一模态或硬编码，缺乏系统性和扩展性。面对复杂接触任务，如何设计高效、稳健的多模态融合架构，成为学界关注焦点。本文在此背景下提出了MoSS，旨在突破现有局限，实现多模态信息的高效融合与操控性能的显著提升。
Core Problem
当前VLAs在复杂接触任务中的表现受限，主要因缺乏多模态感知能力，导致在细粒度操控、接触识别和动态调整方面效果不佳。单模态模型难以充分利用触觉和力矩信息，存在信息冲突、干扰和融合效率低的问题。此外，模型在面对多模态信息时容易出现特征干扰，影响动作生成的稳定性和鲁棒性。如何设计一种既能保持预训练模型优势，又能高效融合多模态信号的架构，成为关键难题。解决此问题对于实现自主、精细操作的机器人具有重要意义。
Innovation
提出解耦多模态传感流架构，避免不同模态干扰预训练特征，提升融合效果。引入联合跨模态自注意机制，实现多模态信号的高效交互，增强模型对复杂接触动态的理解。采用两阶段训练策略，第一阶段冻结预训练模型，只优化新模态流，保证模型稳定；第二阶段端到端微调，结合未来信号预测任务，提升动态建模能力。创新性地将扩散模型引入动作生成，结合多模态信息实现高质量、鲁棒的操控策略。这些创新共同推动机器人在复杂环境中的自主操作能力。
Methodology
- �� 构建多模态传感流，每个模态（如触觉、扭矩）对应独立的流结构，输入为传感信号和预训练的视觉-语言表示。• 在Transformer架构中，将每个模态的自注意力层替换为联合跨模态自注意力层，实现信号间的信息交互。• 采用两阶段训练：第一阶段冻结预训练模型，只训练新模态流参数；第二阶段解冻全部参数，进行端到端微调。• 引入未来信号预测辅助任务，利用历史信号预测未来信号，增强动态理解。• 利用扩散模型进行动作采样，结合多模态信息引导动作生成，提升操控精度。
Experiments
在四个真实接触任务（拆杯、蛋、白板擦、插头）中评估，硬件平台为Franka机械臂配备触觉和扭矩传感器。基线模型包括单模态和现有多模态模型（如Tactile-VLA、ForceVLA）。评价指标为成功率，采用交叉验证和统计分析。通过消融实验验证解耦结构、两阶段训练和未来信号预测的贡献。模型调优包括学习率、训练轮数和模态流参数初始化，确保公平比较。结果显示，MoSS在所有任务中均优于对比模型，成功率提升明显。
Results
在四个任务中，MoSS平均成功率达49.0%，比单模态模型提升超过20%。‘拆杯’任务成功率由20.8%提升至54.2%，‘白板擦’由20.8%提升至41.7%。多模态信号结合后，性能提升更为显著，验证了信号协同作用。消融实验表明，解耦结构、两阶段训练和未来信号预测是性能提升的关键因素。模型在不同硬件平台上表现一致，推理延迟仅增加2.4毫秒，显示出良好的实用性。
Applications
该方法适用于工业自动化、服务机器人等场景，尤其在需要细粒度接触和力控的任务中表现优异。依赖多模态传感器，结合预训练模型，实现高效、稳健的操控策略。未来可扩展到更多传感模态，提升自主适应能力，推动机器人在复杂环境中的自主操作与协作。
Limitations & Outlook
传感器耐久性和噪声问题可能影响长期稳定性，需硬件优化。模型在未充分接触或初始抓取失败时，表现有限，需结合视觉和路径规划改进。多模态融合增加计算负担，需优化推理效率。未来应关注传感器维护、系统鲁棒性和扩展性。
Plain Language Accessible to non-experts
想象你在厨房做饭，手里拿着各种工具：刀、锅、勺子。每个工具都能告诉你一些信息，比如刀告诉你切菜的力度，锅告诉你火候是否合适。传统机器人就像只看镜子里的自己，只能用眼睛判断，做菜时容易出错。现在，加入这些工具的“感官”——比如手上的触觉传感器和手臂的力矩传感器，就像你用手感受食材的软硬、压力大小。这样，机器人就能更像人一样，凭借多种感官信息，做出更精准的动作，比如轻轻夹起一片叶子或用力按压一个按钮。这种多感官融合，让机器人变得更聪明、更灵巧，能应对复杂的厨房任务。
ELI14 Explained like you're 14
Imagine you're playing a super tricky puzzle game. Just looking at the picture isn't enough—you also need to feel the pieces with your hands to see if they fit. Sometimes, you press gently, sometimes harder, depending on how the pieces feel. Robots used to just look at the puzzle, so they often made mistakes, especially when touching was important. Now, scientists gave robots special 'hands' with sensors that tell them how hard they're pressing or if they're touching something. With these new senses, robots can do things like pick up fragile eggs without breaking or insert plugs into sockets more accurately. It's like giving robots a sense of touch and force, so they can handle delicate or tricky tasks just like humans. This makes robots smarter and more capable of working in real-world, contact-rich environments.
Glossary
Vision-Language-Action Model (VLAs) (视觉-语言-动作模型)
一种结合视觉和语言信息，用于指导机器人动作的深度学习模型。它通过预训练获得丰富的感知和理解能力。
本文中的VLAs作为基础架构，扩展以融合多模态物理信号。
Cross-Modal Self-Attention (跨模态自注意机制)
一种机制，使不同模态（如触觉、力矩）之间的信息可以相互关注和交互，提升多模态融合效果。
用于实现多模态信号在模型中的高效交互。
Diffusion Model (扩散模型)
一种生成模型，通过逐步去噪实现高质量样本生成，应用于动作预测和生成。
在本文中用于引导多模态动作生成。
Two-Stage Training (两阶段训练)
先冻结预训练模型参数，只训练新模态流；后解冻全部参数进行微调，确保模型稳定。
保证多模态融合的效果和模型的稳定性。
Future Signal Prediction (未来信号预测)
模型通过预测未来一段时间的物理信号，增强对接触动态的理解。
作为辅助任务提升模型对接触交互的理解能力。
Open Questions Unanswered questions from this research
- 1 如何提升多模态传感器的耐久性和抗噪声能力，以适应工业环境中的长时间使用。
- 2 在未充分接触或初始抓取失败情况下，模型如何更好地进行自我修正和补偿。
- 3 多模态信息融合的计算成本优化，确保在复杂场景中的实时性能。
Applications
Immediate Applications
工业装配机器人
利用多模态感知实现高精度装配，减少误差，提高效率，适用于汽车、电子等行业。
服务机器人
在家庭或医院中进行细致操作，如搬运易碎物品或辅助康复，增强人机交互体验。
Long-term Vision
自主复杂操作系统
实现机器人在未知环境中自主学习和适应多模态信息，完成复杂任务，推动智能制造和自动化。
Abstract
Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approaches attempt to incorporate physical sensory signals into Vision-Language-Action models (VLAs). However, they typically focus on a single type of physical signal, failing to capture the heterogeneous and complementary nature of real-world interactions. In this paper, we propose MoSS, a modular sensory stream framework that adapts VLAs to leverage multiple sensory signals for action prediction. Specifically, we introduce decoupled modality streams that integrate heterogeneous physical signals into the action stream via joint cross-modal self-attention. To enable stable incorporation of new modalities, we adopt a two-stage training scheme that freezes pretrained VLA parameters in the early stage. Furthermore, to better capture contact interaction dynamics, we incorporate an auxiliary task that predicts future physical signals. Through extensive real-world experiments, we demonstrate that MoSS successfully augments VLAs to leverage diverse physical signals (i.e., tactile, force, and torque), integrating multiple signals to achieve synergistic performance gains.