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近半年力觉在具身机器人领域的发展

Progress in Force Sensing for Robotics

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先读这个判断

本文考察 2026 年 1 月 20 日至 7 月 20 日间，力觉在具身机器人中的方法演进。这里的“力觉”不只指腕部六维力/力矩传感器，还包括能反映局部接触力、滑移和形变的视觉触觉阵列，以及由关节、本体或多传感器信号估计出的接触状态。三者提供的空间尺度不同：力/力矩是低维、全局的合力与合力矩，触觉是高维、局部的接触分布，二者不能互换。

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研究综述

研究边界

本文考察 2026 年 1 月 20 日至 7 月 20 日间，力觉在具身机器人中的方法演进。这里的“力觉”不只指腕部六维力/力矩传感器，还包括能反映局部接触力、滑移和形变的视觉触觉阵列，以及由关节、本体或多传感器信号估计出的接触状态。三者提供的空间尺度不同：力/力矩是低维、全局的合力与合力矩，触觉是高维、局部的接触分布，二者不能互换。

本轮检索得到 176 篇候选，46 篇具有完整、可解析的非 OCR 全文，最终纳入 17 篇完成全文精读和主张支持审计的论文。证据以操作、灵巧手和四足全身操作为主，多为近期预印本；尚不足以证明跨传感器、跨本体和长期部署条件下的通用增益。

中心判断

近半年的关键变化不是“机器人又多装了一种传感器”，而是力觉开始成为连接感知、预测与控制的接触状态接口。研究路线正在从把力或触觉作为静态附加观测，转向三层闭环：先把原始信号对齐为可解释的接触表征，再预测动作将如何改变接触，最后用高频反馈修正动作。最强的实验收益并不来自模态数量，而来自信号是否在正确的时间尺度、坐标系和控制层级中发挥作用。

1. 表征层：从触觉图像走向物理接触变量

视觉擅长回答“物体在哪里、要做什么”，却难以稳定观测遮挡后的卡滞、滑移、接触法向和微小错位。近期工作因此不再满足于把触觉图像送进一个视觉编码器。HapTile 把触觉图像之外的 marker displacement 保存为接触几何和滑移线索；TacForeSight 则把腕部六维力/力矩作为未来触觉 latent 的先行条件，其消融中，wrist wrench 条件使预测 MSE 从 0.027 降到 0.017、余弦相似度从 0.954 提高到 0.992。这里的进步是把“感觉到碰了什么”改写为“当前合力如何预示下一段局部接触变化”。

空间对齐同样关键。RGB-S 将稀疏触觉接触投影到图像域，并用力调制的高斯显著图表达运动学和标定误差。这说明跨模态融合不是把两个 token 序列拼接，而是要显式处理坐标与不确定性。另一方面，You Only Touch Once 展示了触觉在视觉不可靠条件下的另一种角色：一次双触点接触也能补充 6-DoF 物体位姿观测，尤其针对遮挡、弱光、反光和透明表面。

更系统的证据来自 ContactWorld。在 12 个接触丰富任务中，点云将平均规划成功率由腕部视角的 20.7% 和前视视角的 22.0% 提高到 32.1%，加入触觉力场后进一步达到 36.1%。这组结果的含义不是“触觉总能再加四个百分点”，而是空间结构、时间连续性和跨模态兼容性共同决定了力觉是否有用。

2. 预测层：力觉开始进入世界模型和动作验证

第二个转变是从反应式反馈走向动作条件下的接触预测。Visuo-Tactile World Models 已把腕部位姿、关节位置、外部视觉和双指尖触觉视频同步成训练序列；Dream-Tac 进一步联合生成未来视觉、未来触觉和动作 chunk，把世界模型扩展为 world action model。

预测的价值并不只在训练期。Inference-time Policy Steering via Vision and Touch 用视觉—触觉 latent world model 对候选动作进行长时域视觉选择和短时域触觉细化，说明力觉模型可以在推理时承担“动作是否会导致错误接触”的验证器。TACO 则把真实失败附近的状态转成视频—力联合想象片段并标注纠正动作，用于 VLA 后训练。两者共同把力觉从结果记录器推进到后果预测和失败修正环节。

但这种融合有明确反例。Tactile-WAM 指出，无约束的触觉 token 注入会产生 tactile pollution：稀疏、局部、事件式的接触信号可能破坏原有视频动态和动作预测。由此可见，力觉世界模型的核心问题不是“有没有触觉分支”，而是视觉与接触动力学是否被恰当地解耦，再在需要的地方交互。

3. 控制层：慢语义、快接触与分层闭环

接触变化通常比语言规划快得多，因此近半年架构越来越强调时间尺度分层。AT-VLA 将低频视觉语言理解与高频触觉反应拆成快慢双流；TouchWorld 也把视觉语言子任务规划、触觉世界模型预测和高频触觉残差修正分开。这比让一个统一模型以同一频率处理“把杯子放进柜子”和“夹持力正在下降”更符合控制问题的结构。

这种思想已经从末端执行器扩展到全身操作。Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies 在保持高层触觉策略不变时，仅加入低层 tactile-command tracking，就把插入成功率从 0.70 提高到 0.85、完整重定向加插入从 0.60 提高到 0.80、阀门拧紧从 0.80 提高到 0.85。TAC-LOCO 的 10 次匹配真机实验中，触觉驱动策略成功率为 90%，固定夹爪的 Deep WBC 基线为 50%。证据虽然样本仍小，却清楚指出：当夹持变化影响身体稳定和运动可达性时，力觉必须同时进入夹爪、手臂和机体控制，不能只在高层做接触分类。

4. 数据与评测：力觉系统的瓶颈正在从模型转向接口工程

力觉比视觉更依赖同步、标定、传感器实例和接触过程。TAMEn 用动捕精度模式与 VR 便携模式平衡数据质量和环境多样性，并把人在环恢复数据纳入采集配方；Multisensory Continual Learning 则主张以少量新传感数据适配既有视觉策略，因为力觉、触觉和音频都具有明显的硬件与任务依赖。合理的数据策略不是预训练时穷尽一切传感器，而是保留视觉先验，再围绕目标硬件、接触阶段和失败恢复做小规模高价值补充。

评测也必须从“任务完成”转向“物理过程是否可接受”。SoftVTBench 将 Goal Success 与 Safety Success 分开，后者同时约束掉落和峰值形变；实验显示，只看最终完成会显著高估策略表现。对于力觉系统，至少还应报告过力次数、滑移/掉落、形变、接触检测延迟、恢复率、跨传感器实例退化和力控稳定性。

可操作的系统分层

层级主要信号适合承担的功能关键风险
语义与几何层RGB、深度、点云、语言目标理解、接触前规划、全局定位遮挡后状态不可见
接触表征层六维力/力矩、触觉图像、marker/force map接触检测、滑移、法向、局部形变坐标和时间未对齐
接触预测层动作条件的视觉—触觉—力序列预判卡滞、候选动作验证、失败片段生成触觉污染视觉动态
快速控制层高频 wrench、触觉残差、关节状态力位混合控制、抓持调节、反射修正延迟、漂移、硬件特定性
安全评测层过力、掉落、形变、恢复轨迹验收过程安全而非只验终点成功率掩盖物理伤害

条件、分歧与研究空白

第一，现有收益高度依赖具体传感器、任务和标定，不能从单一指尖传感器或少量装配任务外推成通用具身能力。第二，世界模型预测出逼真的触觉或视觉未来，不等于动作响应和接触力正确；其结果仍需真实闭环验证。第三，力觉数据的跨实例漂移、磨损、换件重标定和长期维护，在本轮接纳证据中覆盖不足。第四，四足全身操作已有明显真机增益，但实验规模仍小，尚缺统一的扰动强度、负载、表面和失败恢复基准。

下一步最值得验证的不是“力觉能否提高平均成功率”，而是三项更严格的问题：何种接触隐变量必须由力觉补足；何种时间尺度和控制层级应消费该信号；收益能否跨传感器实例、任务和长期运行保持。只要这三项不能被分账，更多模态仍可能只是更复杂的系统，而不是更可靠的机器人。

结论

近半年力觉研究的主线，是把接触从不可见的执行副作用，升级为可表征、可预测、可验证和可调节的状态。真正有前景的系统不是“视觉模型加一个力传感器”，而是视觉负责语义与接触前规划，力觉负责接触后的物理状态，世界模型预测接触演化，高频控制完成局部纠偏，并以安全过程指标验收。这个分层闭环比单纯扩大多模态模型，更接近力觉在具身机器人中的长期价值。

References

Accepted evidence

去重论文引用

  1. AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action ModelsXiaoqi Li, Muhe Cai, Jiadong Xu, Juan Zhu, Hongwei Fan, Yan Shen, Guangrui Ren, Hao Dong · 2026
  2. ContactWorld: What Matters in Vision-Tactile World Models for Contact-Rich ManipulationZhiyuan Zhang, Pokuang Zhou, Kaidi Zhang, Adeesh Desai, Temitope Amosa, Davood Soleymanzadeh, Jiuzhou Lei, Minghui Zheng et al. · 2026
  3. Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot ManipulationYunfan Lou, Yifan Ye, Yankai Fu, Jun Cen, Xiaowei Chi, Yaoxu Lyu, Peidong Jia, Sirui Han et al. · 2026
  4. HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation LearningAmirhosein Alian, Yongqiang Zhao, Shiyi Gu, Xuyang Zhang, Zhuo Chen, Christopher E. Mower, Haitham Bou-Ammar, Shan Luo · 2026
  5. Inference-time Policy Steering via Vision and TouchYilin Wu, Zilin Si, Zeynep Temel, Oliver Kroemer, Andrea Bajcsy · 2026
  6. Learning Tactile-Aware Quadrupedal Loco-Manipulation PoliciesPokuang Zhou, Yuhao Zhou, Quan Khanh Luu, Seungho Han, Heng Zhang, Binghao Huang, Yunzhu Li, Arash Ajoudani et al. · 2026
  7. Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to ForceJaden Clark, Changhao Wang, Yihuai Gao, Seongheon Hong, Hojung Choi, Mark Cutkosky, Yifan Hou, Shuran Song · 2026
  8. RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous ManipulationShengcheng Luo · 2026
  9. SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable ObjectsBowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou et al. · 2026
  10. TAC-LOCO: Unified Whole-Body Control for Quadrupedal TACtile-Informed LOCO-ManipulationMuqun Hu, Yuhao Zhou, Kabir Ray Malik, Chi Lin, Won Suk Lee, Yu She, Yan Gu · 2026
  11. TacForeSight: Force-Guided Tactile World Model for Contact-Rich ManipulationYujie Zang, Yuhang Zheng, Xian Nie, Yupeng Zheng, Shuai Tian, Songen Gu, Chen Gao, Zining Wang et al. · 2026
  12. TACO: TActile World Model as a Self-COrrector forScalable VLA Post-TrainingShengbang Liu, Yueru Jia, Yuyang Yan, Jiaming Liu, Xinran Zhang, Qiuxuan Feng, Yandong Guo, Shiji Zhou et al. · 2026
  13. Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric AttentionSiyu Wu, Linjing You, Junjie Zhu, Yaozu Liu, Changhao Zhang, Jian Liu, Weiqiang Wang, Qi Li et al. · 2026
  14. TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich TasksLongyan Wu, Jieji Ren, Chenghang Jiang, Junxi Zhou, Shijia Peng, Ran Huang, Guoying Gu, Li Chen et al. · 2026
  15. TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous ManipulationJianyi Zhou, Feiyang Hong, Yunhao Li, Yicheng Zhao, Yongjue Cen, Zirui Liu, Jiakang Huang, Zirui Chen et al. · 2026
  16. Visuo-Tactile World ModelsCarolina Higuera, Sergio Arnaud, Byron Boots, Mustafa Mukadam, Francois Robert Hogan, Franziska Meier · 2026
  17. You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile ContactPengfei Ye, Yuxiang Ma, Haonan Chen, Guangming Wang, Yixiong Jing, Brian Sheil, Yilun Du, Edward Adelson · 2026
完整证据附录（17 条正式证据事件）
  • Time range: 2026-01-20..2026-07-20
  • Events: 17
  • 每个事件一节,标题即锚点;trace-map 中的 event 链接跳转到这里。

EA-TWM-READ-0008

  • Claim: TAMEn 用动捕精度模式与 VR 便携模式平衡数据质量和环境多样性，并把人在环的触觉可视化恢复数据纳入金字塔式数据配方。
  • Stance: support | Confidence: direct
  • Paper: 2604.07335 TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks
  • Locator: Abstract (full-text section)
  • Evidence: 摘要明确列出精度/便携双模式采集、触觉恢复遥操作和人在环恢复数据。
  • Quote: “To balance data quality and environmental diversity, we implement a dual-mode acquisition pipeline: a precision mode leveraging motion capture for high-fidelity demonstrations, and a portable mode utilizing VR-based tracking for in-the-wild acquisition and tactile-visualized recovery teleoperation. Building on this hardware, we unify large-scale tactile pretraining, task-specific bimanual demonstrations, and human-in-the-loop recovery data into a pyramid-structured data regime, enabling closed-l”
  • Authors: longyan-wu; jieji-ren; chenghang-jiang; et al.

EA-TWM-READ-0005

  • Claim: 触觉数据的有效形态不止原始 tactile image，还包括 marker displacement 等显式接触几何与滑移特征。
  • Stance: support | Confidence: direct
  • Paper: 2606.04825 HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
  • Locator: 4.2 Vision-Based Tactile Sensing and Marker Tracking
  • Evidence: HapTile 的每个夹爪手指安装视觉触觉传感器，接触会带来图像变化和 marker displacement；论文把 marker-motion 信号保存进数据集并用于 haptic feedback，实验也比较 vision-only、vision+tactile image 与 vision+tactile+marker 表征。
  • Quote: “We smooth the signal with separate rise and release factors, producing a stable tactile motion estimate. The resulting marker-motion signal is stored in HapTile and is also used to drive haptic feedback.”
  • Authors: amirhosein-alian; yongqiang-zhao; shiyi-gu; et al.

EA-TWM-READ-0002

  • Claim: 触觉世界模型可以被扩展为同时生成未来视觉、未来触觉和动作 chunk 的世界动作模型。
  • Stance: support | Confidence: direct
  • Paper: 2606.08737 Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
  • Locator: Abstract (full-text section)
  • Evidence: Dream-Tac 把 world action model 扩展到触觉，联合建模当前视觉、触觉、语言指令下的未来视觉观测、未来触觉观测和动作 chunk，并加入 contact-gated visuotactile fusion 与 contact-aware attention bias。
  • Quote: “In this paper, we propose Dream-Tac, a unified Tactile-World Action Model that jointly models actions, future visual observations, and tactile dynamics. Specifically, Dream-Tac introduces (i) contact-gated visuotactile fusion to selectively integrate tactile signals and (ii) a contact-aware attention bias to better regulate cross-modal interactions during manipulation.”
  • Authors: yunfan-lou; yifan-ye; yankai-fu; et al.

EA-TWM-READ-0006

  • Claim: 腕部六维力/力矩可作为未来触觉 latent 的先行条件，用于预测短时域接触变化。
  • Stance: support | Confidence: direct
  • Paper: 2606.11184 TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
  • Locator: IV-D 1 World Model Conditioning
  • Evidence: TacForeSight 的 TacForceWM 从双指触觉观测出发，以高频腕部 force/torque 为条件预测短时域触觉 latent dynamics；ablation 中 wrist wrench 条件的未来触觉预测优于无条件版本，MSE 从 0.027 降到 0.017，cosine 从 0.954 提升到 0.992。
  • Quote: “We first ablate the conditioning modality used in the tactile world model. We compare four conditioning designs: no external condition, RGB image condition, robot state condition, and wrist wrench condition. Prediction quality is evaluated using Mean Squared Error (MSE), cosine similarity (Cos), and symmetric KL divergence (KL sym ), which respectively measure element-wise prediction error, latent directional consistency, and latent distribution consistency. As shown in Table II , wrist wrench c”
  • Authors: yujie-zang; yuhang-zheng; xian-nie; et al.

EA-TWM-READ-0003

  • Claim: 在接触丰富操作中，触觉世界模型的关键不是简单增加模态，而是让表征同时具备空间结构、时间连续性和跨模态兼容性。
  • Stance: support | Confidence: direct
  • Paper: 2606.13877 ContactWorld: What Matters in Vision-Tactile World Models for Contact-Rich Manipulation
  • Locator: Abstract (full-text section)
  • Evidence: ContactWorld 在 12 个接触丰富任务上比较视觉与触觉表征；点云把平均规划成功率从腕部视角 20.7% 和前视 22.0% 提升到 32.1%，点云加触觉力场进一步到 36.1%。作者强调触觉效果取决于跨模态表征兼容，而非模态数量本身。
  • Quote: “Abstract Contact-rich manipulation requires world models to reason over complex contact dynamics from multimodal sensory observations. However, it remains unclear which representation properties fundamentally support stable long-horizon planning in contact-rich settings. In this paper, we present ContactWorld, a benchmark and systematic empirical study of vision-tactile world models spanning 12 contact-rich manipulation tasks, including insertion, disassembly, screwing, and exploratory interacti”
  • Authors: zhiyuan-zhang; pokuang-zhou; kaidi-zhang; et al.

EA-TWM-READ-0004

  • Claim: 触觉世界模型也可以在推理期作为候选动作验证器，而不只是训练期的动态模型。
  • Stance: support | Confidence: direct
  • Paper: 2606.14981 Inference-time Policy Steering via Vision and Touch
  • Locator: 5 Experiments
  • Evidence: ViTaL 学习 visuo-tactile latent world model，结合视觉和文本条件触觉 verifier，对候选动作进行长时域视觉模式选择和短时域触觉 refinement；真实机器人任务包括 wiping、insertion 和 pipette transfer。
  • Quote: “We evaluate ViTaL on three real-world contact-rich manipulation tasks. Our experiments are designed to answer three questions: whether multimodal visuo-tactile steering improves policy over unimodal guidance (Sec. 5.1 ); whether a learned latent world model and semantically aligned verifier provides reliable rewards for predicted outcomes (Sec. 5.2 ); and whether the proposed bi-level optimization balances performance and efficiency compared to naive visual-tactile fusion (Sec. 5.3 ). Robot Setu”
  • Authors: yilin-wu; zilin-si; zeynep-temel; et al.

EA-TWM-READ-0001

  • Claim: VT-WM 的训练序列同步记录腕部位姿、关节位置、外部视觉和两个指尖触觉视频，并使用时间戳对齐后降采样训练。
  • Stance: conditional | Confidence: direct
  • Paper: 2602.06001 Visuo-Tactile World Models
  • Locator: B.0.1 Training dataset
  • Evidence: 训练数据段明确列出了同步的本体状态、外部视频与双指触觉视频数据流。
  • Quote: “Each sequence contains multimodal data streams: proprioceptive information (wrist pose, joint positions), exocentric video from the camera, and video from each Digit 360 fingertip sensor. All data streams were synchronized using timestamps and downsampled to 6 FPS for training the world model. Our training dataset for V-WM and VT-WM consists of 124 demonstrations totaling 112k datapoints, with each demonstration averaging 40 seconds. For validation, we use 26 demonstrations spanning all tasks, c”
  • Authors: carolina-higuera; sergio-arnaud; byron-boots; et al.

EA-TWM-READ-0007

  • Claim: 触觉信息用于 VLA 或世界模型时，低频视觉语言理解和高频触觉反馈应在架构上分离。
  • Stance: conditional | Confidence: direct
  • Paper: 2605.07308 AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
  • Locator: 5 Conclusion
  • Evidence: AT-VLA 把系统分为慢速视觉语言流和快速触觉流，慢速流负责任务理解和视觉定位，快速流以高频处理触觉反馈；作者采用 3:1 的快慢流频率比，并在真实接触丰富任务中验证 adaptive tactile injection、tactile gate、adaptive cross-attention 和 reaction dual-stream 的作用。
  • Quote: “In summary, AT-VLA introduces an adaptive framework that seamlessly integrates tactile sensing into vision-language-action models. Through the Adaptive Tactile Injection mechanism, AT-VLA dynamically balances pretrained visual-language knowledge with newly learned tactile representations, preserving model integrity while enhancing action precision. The Tactile Reaction Dual-Stream mechanism further enables rapid, high-frequency tactile responses by decoupling slow perceptual reasoning from fast”
  • Authors: xiaoqi-li; muhe-cai; jiadong-xu; et al.

EA-TWM-READ-0014

  • Claim: 在视觉遮挡或视觉退化下，显式把触觉接触投影到图像域可以改善空间对齐；但这种收益依赖对运动学和标定误差导致的空间不确定性建模。
  • Stance: conditional | Confidence: direct
  • Paper: 2606.08765 RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
  • Locator: Abstract (full-text section)
  • Evidence: 作者称视觉观测不可靠或被遮挡时，稀疏异构触觉与稠密视觉表示的对齐是核心挑战；方法使用正运动学和相机标定投影触觉传感器位置，并用力调制高斯 saliency maps 建模运动学和标定误差带来的空间不确定性。
  • Quote: “We then render force-modulated Gaussian saliency maps to model spatial uncertainty arising from kinematic and calibration errors.”
  • Authors: shengcheng-luo

EA-TWM-READ-0010

  • Claim: 触觉不是简单加 token 就能提升；接触任务中的 RGB 未来可能视觉上合理但物理上不完整，而无约束触觉注入还可能污染视频和动作预测。
  • Stance: conditional | Confidence: direct
  • Paper: 2606.26663 Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention
  • Locator: Abstract (full-text section)
  • Evidence: 作者指出 insertion、assembly、search、reorientation 依赖 slip、jamming、contact normals 和小对齐误差，这些状态在 RGB 中弱可见或不可见；同时他们定义 tactile pollution：无约束触觉 token 注入会迫使视觉 dynamics model 吸收稀疏局部事件式接触信号，从而退化视频和动作预测。
  • Quote: “In contact-rich manipulation, however, visually plausible futures can be physically incomplete: insertion, assembly, search, and reorientation often depend on slip, jamming, contact normals, or small alignment errors that are weakly visible or hidden in RGB. A natural solution is to predict future tactile states, however, we identify tactile pollution , a failure mode where unconstrained tactile-token injection degrades video and action prediction by forcing a visual dynamics model to absorb spa”
  • Authors: siyu-wu; linjing-you; junjie-zhu; et al.

EA-TWM-READ-0011

  • Claim: 力觉/触觉/音频能揭示图像不可直接观测的交互状态；但这些模态硬件和任务依赖强，所以需要在少量多传感数据上适配既有视觉策略，而不是预训练时穷尽所有传感器。
  • Stance: conditional | Confidence: direct
  • Paper: 2606.30988 Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force
  • Locator: Abstract (full-text section)
  • Evidence: 作者称接触丰富任务常依赖 vision 之外的 sensory data，force、tactile 或 audio feedback 能揭示 images 中不可直接观察的 interaction states；但这些模态 hardware- and task-specific，且大规模多传感数据稀缺。他们提出 MuSe，将 limited multisensory data 融入 pretrained vision-only policies，并以 force-torque sensing 做真实任务案例。
  • Quote: “Abstract Robot manipulation often depends on sensory data beyond vision, especially in contact-rich tasks where force, tactile, or audio feedback reveals interaction states not directly visible from images. Yet such modalities are hardware- and task-specific, and large multisensory datasets remain scarce, making it impractical to pretrain policies with every sensor they may encounter. We study multi-sensory continual learning : adapting a pretrained robot policy to new tasks with newly introduce”
  • Authors: jaden-clark; changhao-wang; yihuai-gao; et al.

EA-TWM-READ-0012

  • Claim: 只看任务完成会低估传感器感知误差的真实风险；柔性物操作需要把安全交互、滑移/掉落和过度形变作为独立评测目标。
  • Stance: limit | Confidence: direct
  • Paper: 2607.04234 SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects
  • Locator: 1 Introduction
  • Evidence: 作者指出现有 manipulation benchmarks 多以 success 为中心，很少评估执行过程是否物理安全；SoftVTBench 分开报告 Goal Success 和 Safety Success，后者要求无掉落并限制峰值形变。实验显示 success-only evaluation 会显著高估策略表现，而触觉感知可改善 Safety Success 并降低物体形变。
  • Quote: “Experiments show that success-only evaluation substantially overstates policy performance, as a large fraction of goal-completing rollouts violate physical safety, and that adding tactile sensing improves Safety Success while keeping Goal Success comparable and reduces object deformation during execution.”
  • Authors: bowen-jing; mingxin-wang; ruiyang-hao; et al.

EA-LOCOMANIP-2026-0012

  • Claim: Adding tactile-command tracking at the low level raised insertion success from 0.70 to 0.85, full reorientation-plus-insertion from 0.60 to 0.80, and valve tightening from 0.80 to 0.85.
  • Stance: support | Confidence: direct
  • Paper: 2604.27224 Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
  • Locator: IV-B Experimental Results and Analyze
  • Evidence: The paper compares variants with the same tactile-aware high level but different low-level tactile tracking.
  • Quote: “Finally, comparing Baseline 3 (P3) with our full method demonstrates the benefit of incorporating tactile commands into the low-level policy: our method further increases the success rate from 0.70 to 0.85 on Task 1 insertion (+0.15), from 0.60 to 0.80 on Task 1 whole (+0.20), and from 0.80 to 0.85 on Task 2 (+0.05), and achieves 1.00 on Task 3 (compared to 0.20 for P1).”
  • Authors: pokuang-zhou; yuhao-zhou; quan-khanh-luu; et al.

EA-LOCOMANIP-2026-0021

  • Claim: In 10 matched hardware trials, tactile-informed TAC-LOCO achieved 90% dynamic loco-manipulation success versus 50% for Deep WBC with a fixed gripper.
  • Stance: support | Confidence: direct
  • Paper: 2607.10132 TAC-LOCO: Unified Whole-Body Control for Quadrupedal TACtile-Informed LOCO-Manipulation
  • Locator: 6.5 Baseline comparison
  • Evidence: The hardware baseline comparison isolates learned grasp regulation under the same command set.
  • Quote: “We conduct 10 hardware trials using the same set of loco-manipulation commands as in Sec. 6 . Table 4 compares the success rates of our policy and the baseline, showing that our tactile-informed policy achieves a substantially higher success rate. Figure 11 shows that the baseline suffers from gradual object slip during the task. Since the gripper width remains fixed, the policy cannot actively suppress slip once the external force changes. Table 4: Deployment success rate comparison with baseli”
  • Authors: muqun-hu; yuhao-zhou; kabir-ray-malik; et al.

EA-SENSORERR-READ-0010

  • Claim: 物体 6-DoF 位姿误差在遮挡、弱光、反光/透明表面下会让视觉方法失效；单次双触点触觉可作为视觉不可靠时的位姿观测补充。
  • Stance: support | Confidence: direct
  • Paper: 2606.28899 You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
  • Locator: Abstract (full-text section)
  • Evidence: 作者明确指出视觉位姿估计常在遮挡、差光照、反光或透明表面下失败，并提出 tactile-only pose estimation：把触觉接触表示成局部 3D 点云，结合校准传感器位姿恢复完整 6-DoF object pose；实验在视觉不可靠时优于视觉和几何基线。
  • Quote: “Abstract Accurate 6-DoF object pose estimation is fundamental to robotic manipulation, yet vision-based methods often fail under occlusion, poor lighting, and reflective or transparent surfaces.”
  • Authors: pengfei-ye; yuxiang-ma; haonan-chen; et al.

EA-SENSORERR-READ-0012

  • Claim: 触觉在灵巧操作中补足视觉/语言无法稳定观测的接触隐变量；滑移、力不匹配、接触稳定性等局部误差需要比语义规划更快的反馈通道。
  • Stance: support | Confidence: direct
  • Paper: 2607.07287 TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation
  • Locator: Abstract (full-text section)
  • Evidence: 作者把日常灵巧操作的误差来源明确落在滑移、错位、不稳定抓取和力不匹配上，并指出视觉/语言不能可靠揭示力、滑移和接触稳定性；其分层策略将视觉语言子任务规划、触觉世界模型预测和高频触觉残差修正分开。
  • Quote: “Vision and language provide semantic and geometric guidance, but they cannot reliably reveal hidden contact states such as force, slip, and contact stability.”
  • Authors: jianyi-zhou; feiyang-hong; yunhao-li; et al.

EA-SENSORERR-READ-0001

  • Claim: TACO 用触觉感知世界模型联合预测未来视频和力序列，再将真实失败附近状态转成带纠正动作的想象片段，用于接触丰富任务的 VLA 后训练。
  • Stance: limit | Confidence: direct
  • Paper: 2607.02840 TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training
  • Locator: 5 Conclusion and Limitations
  • Evidence: 结论的 Recognize–Imagine–Label 回路明确连接了真实失败、视频—力联合想象与纠正动作标注。
  • Quote: “We presented TACO, a tactile-aware world-model-driven framework for scalable VLA post-training in contact-rich manipulation. Following a Recognize–Imagine–Label loop, TACO converts real-world failures into imagined corrections without repeated human intervention: a tactile-aware world model jointly denoises future video and force sequences, while a unified progress-action model recognizes failure-adjacent states and labels imagined segments with corrective actions. To incorporate this supervisio”
  • Authors: shengbang-liu; yueru-jia; yuyang-yan; et al.

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