Tactile sensing provides a promising sensing modality for object pose estimation in manipulation settings where visual information is limited due to occlusion or environmental effects. However, efficiently leveraging tactile data for estimation remains a challenge due to partial observability, with single observations corresponding to multiple possible contact configurations. This limits conventional estimation approaches largely tailored to vision. We propose to address these challenges by learning an inverse tactile sensor model using denoising diffusion. The model is conditioned on tactile observations from a distributed tactile sensor and trained in simulation using a geometric sensor model based on signed distance fields. Contact constraints are enforced during inference through single-step projection using distance and gradient information from the signed distance field. For online pose estimation, we integrate the inverse model with a particle filter through a proposal scheme that combines generated hypotheses with particles from the prior belief. Our approach is validated in simulated and real-world planar pose estimation settings, without access to visual data or tight initial pose priors. We further evaluate robustness to unmodeled contact and sensor dynamics for pose tracking in a box-pushing scenario. Compared to local sampling baselines, the inverse sensor model improves sampling efficiency and estimation accuracy while preserving multimodal beliefs across objects with varying tactile discriminability.

翻译：触觉传感为在遮挡或环境影响导致视觉信息受限的操作场景中估计物体姿态提供了一种有前景的感知方式。然而，由于部分可观测性（单次观测对应多种可能的接触构型），有效利用触觉数据进行估计仍具挑战。这限制了主要为视觉量身定制的传统估计方法。我们提出通过使用去噪扩散学习一个逆触觉传感器模型来应对这些挑战。该模型以分布式触觉传感器的观测为条件，并在仿真中使用基于有向距离场的几何传感器模型进行训练。在推理过程中，通过利用有向距离场提供的距离和梯度信息进行单步投影，以强制执行接触约束。对于在线姿态估计，我们通过一种提议方案将逆模型与粒子滤波器集成，该方案将生成的假设与来自先验信念的粒子相结合。我们的方法在仿真和真实世界的平面姿态估计场景中得到了验证，且无需访问视觉数据或严格的初始姿态先验。我们进一步评估了在推箱场景中，该方法对未建模接触和传感器动态的鲁棒性以进行姿态跟踪。与局部采样基线相比，逆传感器模型提高了采样效率和估计精度，同时在不同触觉可区分性的物体上保持了多模态信念。