{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:27Z","timestamp":1776976467030,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>To address the challenges of robotic dexterous manipulation in complex environments, this paper proposes a hierarchical reinforcement learning (HRL) framework driven by tactile information. We design an adaptive hierarchical decision-making algorithm that integrates multimodal tactile features, dynamically adjusting hierarchical strategies and reward functions to adapt efficiently to complex tactile environments. Experiments were conducted on the PyBullet simulation platform, constructing a manipulation scenario involving 20 objects of varying shapes and materials. Two primary tasks were evaluated: basic grasping and placement, and complex assembly. Comparative results against standard DQN, PPO, and existing tactile-driven algorithms demonstrate that the proposed method achieves a success rate of 92.3% in basic tasks\u2014outperforming DQN by 27.6% and PPO by 21.5%\u2014while reducing the average operation time to 3.2 seconds. For complex tasks, the success rate increased to 85.7% (a 31.2% improvement over baselines), with a 40% acceleration in convergence speed. Ablation studies further validate that multimodal tactile feature fusion contributes an 18.3% increase in success rate, while the adaptive adjustment mechanism reduces strategy adjustment time by 35%. These findings confirm that the proposed framework significantly enhances robotic dexterous manipulation performance, offering a new pathway for the development of intelligent robotic systems.<\/jats:p>","DOI":"10.31449\/inf.v50i11.13667","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:10Z","timestamp":1776972970000},"source":"Crossref","is-referenced-by-count":0,"title":["A Tactile-Driven Hierarchical Reinforcement Learning Framework for Dexterous Robotic Manipulation"],"prefix":"10.31449","volume":"50","author":[{"given":"Gao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruchao","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linkun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13667\/6662","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13667\/6662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:11Z","timestamp":1776972971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/13667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.13667","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}