TAPNAV: Humanoid Navigation through Tactile Active Perception

Huaze Liu; Zhenyu Wu; Jaehwi Jang; Junjie Sheng; Andrew Collins; Aaron Xie; Zhaoyuan Gu; Kaijie Zhu

cs.ROOct 7, 2026

TAPNAV: Humanoid Navigation through Tactile Active Perception

Authors: Huaze Liu, Zhenyu Wu, Jaehwi Jang, Junjie Sheng, Andrew Collins, Aaron Xie, Zhaoyuan Gu, Kaijie Zhu, +3 more

Organizations: Georgia Institute of Technology · Harvey Mudd College · Walton High School

Abstract

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.

Figures & tables

Fig. 2: Overview of the TapNav framework. Odometry, IMU, and tactile observations together update the robot’s $\mathrm{SE}(2)$ pose belief. The global planner computes an uncertainty-bounded route with predicted tactile corrections, the local planner selects informative probes based on expected information gain, and the low-level controller executes locomotion and probing.

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.

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 ( $\delta_{t}^{(1)}<0$ ); (c) Under hypothesis $2$ , it would not reach the surface ( $\delta_{t}^{(2)}>0$ ).

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 $(\mathbf{n}_{t}^{a})^{\top}\boldsymbol{\mu}_{t}^{xy}-\mu_{m_{t}^{a}}$ ).

Fig. 6: The low-level controller is composed of an upper- and a lower-body policy. The upper-body policy tracks the target end-effector pose and force, while the lower-body policy tracks footstep commands. The two policies are jointly trained with shared whole-body proprioception.

Fig. 7: TapNav navigation performance in simulation. (a)–(d) correspond to four evaluated maps: (a) a curved corridor, (b) a partitioned room, (c) a zigzag corridor, and (d) a room with scattered obstacles. Each map has its own start (purple) and goal (yellow star). For each map, (i) shows the map layout with the estimated trajectory (blue), the ground-truth trajectory measured from the simulator (black), and contact points from probing (red), where each grid cell is $1\,\text{m}\times 1\,\text{m}$ ; (ii), (iii) show position and yaw uncertainty (blue) and error (black) over time, respectively, with vertical red lines marking contact events. The red and yellow boxes highlight the largest decreases in both estimation error and uncertainty, which occur when the robot contacts non-planar wall surfaces.

Parameter	Setting
Controller Frequency	200 / 50 Hz
Replanning Deviation Margin	$>0.1$ m
Global Planner Grid Size	0.1 m
Minimal Wall Clearance	0.2 m
$\|\Delta\mathbf{x}_{t}\|_{\text{max}}$	0.40 m
$(\bar{\sigma}_{p},\bar{\sigma}_{\psi})$	(0.18 m, $20^{\circ}$ )

TABLE I: Simulation and navigation parameters.

Method	Map a			Map b			Map c			Map d
Method	PosErr	YawErr	SR	PosErr	YawErr	SR	PosErr	YawErr	SR	PosErr	YawErr	SR
	(cm) $\downarrow$	(deg) $\downarrow$	(%) $\uparrow$	(cm) $\downarrow$	(deg) $\downarrow$	(%) $\uparrow$	(cm) $\downarrow$	(deg) $\downarrow$	(%) $\uparrow$	(cm) $\downarrow$	(deg) $\downarrow$	(%) $\uparrow$
TapNav	$\boldsymbol{6.5_{\pm 2.5}}$	$\boldsymbol{1.4_{\pm 0.4}}$	95	$\boldsymbol{9.4_{\pm 3.6}}$	$2.8_{\pm 1.8}$	100	$\boldsymbol{5.9_{\pm 1.9}}$	$2.0_{\pm 1.3}$	100	$16.8_{\pm 5.0}$	$2.3_{\pm 1.6}$	85
Random-touch	$9.6_{\pm 3.8}$	$2.1_{\pm 1.4}$	90	$11.4_{\pm 3.9}$	$2.9_{\pm 1.4}$	70	$8.7_{\pm 3.6}$	$2.9_{\pm 1.7}$	75	$16.6_{\pm 6.0}$	$2.7_{\pm 1.8}$	75
Sweep-touch	$11.7_{\pm 4.5}$	$2.0_{\pm 1.1}$	50	$11.2_{\pm 3.9}$	$\boldsymbol{2.5_{\pm 1.5}}$	50	$10.9_{\pm 4.2}$	$2.4_{\pm 1.6}$	30	$\boldsymbol{13.7_{\pm 3.5}}$	$\boldsymbol{1.32_{\pm 0.1}}$	25
Odometry-only	$23.4_{\pm 11.2}$	$3.5_{\pm 2.9}$	35	36.5	4.0	5	13.2	0.6	5	$28.5_{\pm 12.2}$	$4.0_{\pm 4.6}$	45

TABLE II: MuJoCo simulation results. The navigation performance of TapNav and the baselines is evaluated across four maps. Errors are reported as mean

{}_{\pm\text{std}}

; entries without a standard deviation correspond to settings in which only a single trial succeeded.

Fig. 8: Yaw-noise sensitivity. (a) position error; (b) yaw error; (c) success rate. Bands and bars show mean $\pm$ standard error in (a)–(b) and 95% confidence intervals in (c).

Fig. 9: Simulated open-loop goal tracking with velocity- and footstep-tracking policies (30 trials per policy). (a) Executed trajectories, highlighting the trial with median final position error for each policy; (b) Actual and commanded heading over time.

Fig. 10: TapNav deployed on a Unitree G1 humanoid equipped with our tactile end-effectors, demonstrating real-world navigation capabilities in (a) a kitchen environment and (b) an office hallway. For each environment, (i) shows snapshots of the real-world deployment; (ii) shows the robot’s path and active tactile probes in the robot’s egocentric view.

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RL for Robotics Robotic Tactile Sensing

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Humans effortlessly locate and identify objects by touch alone, even without vision. In contrast, robotic systems rely heavily on vision and struggle with autonomous tactile exploration and object identification. We present TACTFUL, a vision-free tactile exploration framework that enables a multi-fingered robot to autonomously explore confined workspaces, discover objects through contact, and identify them via tactile reconstruction. Trained entirely on real hardware without simulation, our system learns a single policy that balances global workspace exploration with local surface refinement through a dynamic reward schedule. Our results demonstrate that tactile sensing, when paired with structured learning, can serve as an effective primary modality for object-level reasoning, achieving 77% success with 0.015 m average reconstruction error and outperforming baseline approaches on real-world objects.

Abstract

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