{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T12:13:08Z","timestamp":1777723988343,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aims to enhance the precision of humanoid robots in imitating complex human \u201cwalking\u2013grasping\u201d coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll\/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control\u2014guided by zero moment point (ZMP) and roll\/pitch data\u2014and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments.<\/jats:p>","DOI":"10.3390\/s26092756","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:34:42Z","timestamp":1777458882000},"page":"2756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Humanoid Robot Walking and Grasping Method Using Similarity Reward-Augmented Generative Adversarial Imitation Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Gen-Yong","family":"Huang","sequence":"first","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Feng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","first-page":"2817","article-title":"Research Status of Robot Grasping Detection Technology","volume":"35","author":"Liu","year":"2020","journal-title":"Control Decis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"148411","DOI":"10.1109\/ACCESS.2020.3015506","article-title":"Walking Control of a Biped Robot on Static and Rotating Platforms Based on Hybridn Reinforcement Learning","volume":"8","author":"Xi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1109\/JAS.2019.1911567","article-title":"Balance control of a biped robot on a rotating platform based on efficient reinforcement learning","volume":"6","author":"Xi","year":"2019","journal-title":"IEEE\/CAA J. 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