{"ID":6138077,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T04:12:54.209499018Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06989","arxiv_id":"2607.06989","title":"Ace! Motion Planning of Professional-Level Table Tennis Serves with a Robot Arm","abstract":"Table tennis, a dynamic, compact, and popular sport, has received significant attention as a robotics benchmark over the last decades. Most of the research has focused on the rally aspect - returning an incoming ball - requiring high-speed vision, agile motion planning, and tight closed-loop control. However, the other component of table tennis gameplay - the serve - is comparatively a quite unexplored research problem, that in fact requires pushing physics modeling and control to the extremes. Achieving competitive serves with a robot presents domain-specific challenges, such as high-spin generation from a spinless ball, precise aiming, or multi-objective optimization. In this work, we present a novel approach for generating official rule-compliant serves by combining motion primitives, Model Predictive Control, and Bayesian Optimization. Serves generated in this way offer a wide and controllable variation of spins of up to 550 rad/s, and speeds of up to 6.7 m/s, matching and even surpassing those of elite table tennis players.","short_abstract":"Table tennis, a dynamic, compact, and popular sport, has received significant attention as a robotics benchmark over the last decades. Most of the research has focused on the rally aspect - returning an incoming ball - requiring high-speed vision, agile motion planning, and tight closed-loop control. However, the other...","url_abs":"https://arxiv.org/abs/2607.06989","url_pdf":"https://arxiv.org/pdf/2607.06989v1","authors":"[\"Guillem Torrente\",\"Guilherme Jorge Maeda\",\"Divij Grover\",\"Megumu Tsukamoto\",\"Hamdi Sahloul\",\"Peter Dürr\"]","published":"2026-07-08T04:23:30Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}