SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles

Junchi Gu; Feiyang Yuan; Weize Shi; Tianchen Huang; Haopeng Zhang; Xiaohu Zhang; Yu Wang; Wei Gao

cs.ROJan 8, 2026

SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles

Authors: Junchi Gu, Feiyang Yuan, Weize Shi, Tianchen Huang, Haopeng Zhang, Xiaohu Zhang, Yu Wang, Wei Gao,

Organizations: Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China.

Abstract

Although recent years have seen significant progress of humanoid robots in walking and running, the frequent foot strikes with ground during these locomotion gaits inevitably generate high instantaneous impact forces, which leads to exacerbated joint wear and poor energy utilization. Roller skating, as a sport with substantial biomechanical value, can achieve fast and continuous sliding through rational utilization of body inertia, featuring minimal kinetic energy loss. Therefore, this study proposes a novel humanoid robot with each foot equipped with a row of four passive wheels for roller skating. A deep reinforcement learning control framework is also developed for the swizzle gait with the reward function design based on the intrinsic characteristics of roller skating. The learned policy is first analyzed in simulation and then deployed on the physical robot to demonstrate the smoothness and efficiency of the swizzle gait over traditional bipedal walking gait in terms of Impact Intensity and Cost of Transport during locomotion. A reduction of $75.86\%$ and $63.34\%$ of these two metrics indicate roller skating as a superior locomotion mode for enhanced energy efficiency and joint longevity.

Figures & tables

Fig. 1: Overview of the SKATER platform and its learned swizzling gait.

Fig. 2: Hardware design of SKATER and its passive-wheel inline-skate foot module.

Fig. 3: Deep reinforcement learning control framework for SKATER. The policy network processes proprioceptive sensor data to generate joint-level commands, enabling adaptive inline skate swizzles.

Task Rewards
Reward Term	Formula	Weight
Alive	$\mathbf{1}_{\mathrm{alive}}$	$0.15$
Linear Velocity Track	$\exp\left(-\frac{\\|\mathbf{v}_{xy}^{\mathrm{cmd}}-\mathbf{v}_{xy}^{\mathrm{yaw}}\\|^{2}}{0.25}\right)$	$1.8$
Angular Velocity Track	$\exp\left(-\frac{(\omega_{z}^{\mathrm{cmd}}-\omega_{z}^{\mathrm{base}})^{2}}{0.25}\right)$	$1.2$
Joint Penalties
Arms Deviation	$\sum_{\mathrm{arms}}\|q_{i}-q_{i}^{0}\|$	$-0.4$

TABLE I: Reward Function Terms

Term	Value
Material Physics - Wheels
Static Friction Coefficient	$\mathcal{U}(0.1,0.8)$
Dynamic Friction Coefficient	$\mathcal{U}(0.1,0.4)$
Base Mass Offset	$\mathcal{U}(-1.0,3.0)$ kg added to pelvis
CoM Offset - Pelvis and Torso
$x/y/z$ axes	$\mathcal{U}(-0.01,0.01)$ m

TABLE II: Domain Randomization Parameters

Fig. 4: Comparison of vertical GRF profiles.

Fig. 5: Velocity tracking performance of SKATER following step commands.

Fig. 6: Temporal analysis of the learned swizzling gait: ankle joint angle profiles and inter-foot distance variation with the reward bounds indicated.

(a) Main performance metrics
Method	Task Survival (%) $\uparrow$	Worst-cmd. Survival (%) $\uparrow$	Fall Rate (%) $\downarrow$	Forward Vel. RMSE (m/s) $\downarrow$	Yaw-rate RMSE (rad/s) $\downarrow$	Energy per Meter $\downarrow$
Full reward	94.62 $\pm$ 0.75	64.80 $\pm$ 9.09	5.38 $\pm$ 0.75	0.236 $\pm$ 0.001	0.194 $\pm$ 0.001	113.95 $\pm$ 0.27
w/o symmetry reward	99.17 $\pm$ 0.41	93.00 $\pm$ 3.08	0.83 $\pm$ 0.41	0.257 $\pm$ 0.000	0.329 $\pm$ 0.000	140.13 $\pm$ 0.11
w/o ZMP reward	87.22 $\pm$ 1.13	26.40 $\pm$ 2.70	12.78 $\pm$ 1.13	0.196 $\pm$ 0.001	0.255 $\pm$ 0.002	81.91 $\pm$ 0.61
w/o ZMP margin reward	90.00 $\pm$ 0.93	0.00 $\pm$ 0.00	10.00 $\pm$ 0.93	0.241 $\pm$ 0.003	0.251 $\pm$ 0.000	90.92 $\pm$ 0.84
w/o foot-distance reward	86.19 $\pm$ 0.93	0.00 $\pm$ 0.00	13.81 $\pm$ 0.93	0.273 $\pm$ 0.001	0.326 $\pm$ 0.004	154.34 $\pm$ 1.15

TABLE III: Reward term ablations. Values are reported as mean

\pm

standard deviation over five random seeds.

Fig. 7: Horizontal foot trajectories under representative reward term ablations and their motion-level failure modes.

Panel of Figure 11 — Fig. 8: Real-world mobility validation: (a) obstacle-crossing swizzling and (b) high-speed flat-ground traversal.

Joint Name	Swizzling(N·m)	Walking(N·m)	Difference (%)
L Hip Pitch	$20.86\pm 6.22$	$68.47\pm 1.91$	$-$ 69.53
L Hip Roll	$56.22\pm 4.08$	$60.62\pm 2.25$	$-$ 7.26
L Hip Yaw	$23.18\pm 1.39$	$33.82\pm 1.19$	$-$ 31.46
L Knee	$49.48\pm 2.98$	$51.76\pm 6.84$	$-$ 4.41
L Ankle Pitch	$13.58\pm 2.51$	$46.37\pm 6.10$	$-$ 70.71
L Ankle Roll	$11.80\pm 2.77$	$11.08\pm 1.84$	$+$ 6.55

TABLE IV: Comparison of peak joint torques between swizzling and walking

Fig. 9: Energy efficiency results: joint-level energy consumption and mechanical Cost of Transport comparison between swizzling and walking gaits.

Robot Locomotion

Robot locomotion studies how robots move efficiently and reliably across varied terrains, environments, and movement modes.

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Jun 30, 2026J/K move · Enter open · S save

Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot

Ethan Marot, Thomas Bi, Clemens Schwarke, Victor Klemm+2

Institute for Dynamic Systems and Control, ETH Zurich, Switzerland. · Robotic Systems Lab, ETH Zurich, Switzerland

Humanoid Robot Locomotion Robot Locomotion RL for Legged Locomotion

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As humanoid robots become increasingly dynamic, coupling them with reinforcement learning offers a promising approach to solving the complex, underactuated mechanics of passive inline skating. Equipping a humanoid robot with passive inline skating wheels presents an opportunity to combine the versatile agility of humanoids with the high-speed, energy-efficient locomotion strategies utilized by human skaters. In this paper, we train and deploy a reinforcement learning control policy that enables novel locomotion strategies for a humanoid robot modified to equip consumer inline skates instead of conventional feet. Unlike previous work limited to quadrupedal robots or actively driven wheels, our system allows for precise 6-DoF control of the skates to execute dynamic, edge-driven propulsion strategies. Our skating strategies emerge entirely from our reward structure, without reliance on human motion data, imitation learning, or kinematic priors. We overcome the inherent instability of passive wheels and simulation contact artifacts by utilizing different geometric wheel models (spherical and ellipsoidal) during training and validation, along with a custom success-based command curriculum and a specialized rolling reward. Consequently, our policy demonstrates up to a 50% reduction in Cost of Transport (CoT) compared to standard walking gaits. The resulting policy successfully transfers zero-shot to the physical Booster T1 hardware. Real-world deployments demonstrate dynamic balance, the ability to reject active physical perturbations, and agile locomotion strategies capable of turning at speed. A video of our results can be found at https://www.youtube.com/watch?v=-_APcOS7uFo.

Abstract

Robot Locomotion

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