TAPNAV: Humanoid Navigation through Tactile Active Perception

Huaze Liu, Zhenyu Wu, Jaehwi Jang, Junjie Sheng, Andrew Collins, Aaron Xie, Zhaoyuan Gu, Kaijie Zhu, Ding Jiang, Kevin Cai, Ye Zhao · N/A · 2026

Summary

Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly.

Abstract Summary

Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly. We present TAPNAV, a tactile active-perception framework that enables humanoid navigation toward a goal by actively probing surrounding structures without relying on vision. TAPNAV maintains a pose belief from odometry, IMU, and tactile contact observations, and couples uncertainty-aware global route planning with information-gain-driven local probing. The global planner searches for routes that keep predicted localization uncertainty bounded by exploiting opportunities for tactile correction, while the local planner selects probe actions that maximize expected information gain. A whole-body controller coordinates the humanoid's locomotion and end-effector contact to execute the planned navigation and probe motions. We evaluate TAPNAV in simulation and on a Unitree G1 across different floor plans and obstacle geometries. TAPNAV achieves lower state estimation error and a higher task completion rate than baselines. These results demonstrate that actively planning physical interactions with the environment can provide localization cues for reliable humanoid navigation without vision.

Key Points

Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly.
We present TAPNAV, a tactile active-perception framework that enables humanoid navigation toward a goal by actively probing surrounding structures without relying on vision.
TAPNAV maintains a pose belief from odometry, IMU, and tactile contact observations, and couples uncertainty-aware global route planning with information-gain-driven local probing.
The global planner searches for routes that keep predicted localization uncertainty bounded by exploiting opportunities for tactile correction, while the local planner selects probe actions that maximize expected information gain.
A whole-body controller coordinates the humanoid's locomotion and end-effector contact to execute the planned navigation and probe motions.

System Overview

We formulate the problem as a vision-denied humanoid navigation task on a known and static map $M$. The robot starts from a known initial state $\mathbf{x}_{0}$, its state at time $t$ is represented by $\mathbf{x}_{t}=(p^{x}_{t},p^{y}_{t},\psi_{t})\in\mathrm{SE}(2)$ . We localize the robot using odometry, an IMU-based heading estimate, and tactile contact with mapped surface.

The goal is to reach a target region $\mathcal{X}_{g}$ while balancing route progress with active tactile probing to control localization uncertainty.

System Overview — TAPNAV: Humanoid Navigation through Tactile Active Perception — Fig. 3: Tactile end-effector design and contact-processing pipeline. The tactile image is aggregated into a total activation $T(t)$, which is calibrated to a normal force and converted to a binary contact outcome using a force threshold.

Method

The Unitree G1 is equipped with custom tactile end-effectors to detect contact with the environment, as shown in Fig. 3 . Each end-effector integrates a TouchTronix SensX 160 tactile sensor covered by a silicone pad made from Ecoflex 00-50 [ 22 ] . The SensX 160 consists of a $20\times 8$ taxel array with an active sensing area of $20\times 50$ mm and a force range of up to 100 N.

The sensor is mounted on a curved, rigid housing to enable contact sensing across a range of approach angles.

Method — TAPNAV: Humanoid Navigation through Tactile Active Perception — Fig. 4: Tactile likelihood. (a) The residual $\delta^{(i)}_{t}$ under hypothesis $i$ and the nearest point on the map surface $\mathbf{c}_{t}^{a}$ defined in Sec. IV-C ; (b) Under hypothesis $1$, the end-effector would penetrate the surface ($\delta_{t}^{(1)} 0$).

Experiments

We evaluate TapNav in simulation and on a Unitree G1 humanoid using the parameters in Table I . The experiments address three questions: Q1 : Does TapNav provide reliable and safely bounded state estimation throughout navigation across different maps? Q2 : How accurately does the low-level controller adjust the robot’s position throughout navigation? Q3 : Can TapNav perform reliably in real-world navigation tasks?

All simulation experiments are conducted in MuJoCo. To address Q1 , TapNav is evaluated on four layouts in Fig.

Experiments — TAPNAV: Humanoid Navigation through Tactile Active Perception — Fig. 5: Information gain of one probe action. (a) The margin uncertainty, modeled as $\tilde{m}_{t}^{a}\sim\mathcal{N}(\mu_{m_{t}^{a}},\sigma^{2}_{m_{t}^{a}})$. (b) Pose belief along $\mathbf{n}_{t}^{a}$ before the probe, after a contact, and after a miss (prior truncated at $(\mathbf{n}_{t}^{a})^{\top}\boldsymbol{\mu}_{t}^{xy}-\mu_{m_{t}^{a}}$).

Sources and Demonstrations

Figures are reproduced from Liu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.