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The planner module finds physically feasible foothold plans by sampling-based optimization with fast sequential filtering using heuristics and a neural network. Subsequently, rollouts are performed in a physics simulation to identify the best foothold plan regarding the engineered cost function and to confirm its physical consistency. This hierarchical planning module is computationally efficient and physically accurate at the same time. The tracker aims to accurately step on the target footholds from the planning module. During the training stage, the foothold target distribution is given by a generative model that is trained competitively with the tracker. This process ensures that the tracker is trained in an environment with the desired difficulty. The resulting tracker can overcome terrains that are more difficult than what the previous methods could manage. We demonstrated our approach using Raibo, our in-house dynamic quadruped robot. The results were dynamic and agile motions: Raibo is capable of running on vertical walls, jumping a 1.3-meter gap, running over stepping stones at 4 meters per second, and autonomously navigating on terrains full of 30\u00b0 ramps, stairs, and boxes of various sizes.<\/jats:p>","DOI":"10.1126\/scirobotics.ads6192","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T17:58:11Z","timestamp":1748455091000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":7,"title":["High-speed control and navigation for quadrupedal robots on complex and discrete terrain"],"prefix":"10.1126","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1422-449X","authenticated-orcid":true,"given":"Hyeongjun","family":"Kim","sequence":"first","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8907-3671","authenticated-orcid":true,"given":"Hyunsik","family":"Oh","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7619-1884","authenticated-orcid":true,"given":"Jeongsoo","family":"Park","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4985-8118","authenticated-orcid":true,"given":"Yunho","family":"Kim","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8735-9373","authenticated-orcid":true,"given":"Donghoon","family":"Youm","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7983-818X","authenticated-orcid":true,"given":"Moonkyu","family":"Jung","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4930-1042","authenticated-orcid":true,"given":"Minho","family":"Lee","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3444-8079","authenticated-orcid":true,"given":"Jemin","family":"Hwangbo","sequence":"additional","affiliation":[{"name":"Robotics &amp; Artificial Intelligence Lab, KAIST, Daejeon, Korea."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"B. 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