手術におけるヒューマノイドロボットの生体内実現可能性研究
In vivo feasibility study of humanoid robots in surgery
ヒューマノイドロボットを腹腔鏡手術の遠隔操作に応用し、ベンチトップ評価、ドライラボでのユーザー試験、ブタを用いた生体内試験を通じて、その技術的実現可能性と臨床応用への準備度を体系的に評価した論文。
詳しい要約
1. どんなもの?
2. 先行研究と比べてどこがすごい?
3. 技術・手法の肝は?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Zekai Liang, Nikita Thareja, Peihan Zhang, Calvin Joyce, Soofiyan Atar, Florian Richter, Garth Jacobsen, Shanglei Liu, Ryan Broderick, Michael Yip
分類: cs.RO
原文アブストラクト
Recent advances in actuation, control and learning have rapidly pushed humanoid robots from a distant vision towards near-term real-world deployment. Healthcare is a particularly pressing domain, in which staffing shortages and increasing care demand are widening the gap between clinical workload and available skilled labour. Although current automation has largely focused on digital and logistical tasks, much hospital work remains embodied, requiring mobility, manipulation and safe interaction in human-designed environments. Humanoid form factors offer unique potential, particularly for assisting with surgical tasks. Traditionally, robotic systems for surgery are purpose-built platforms such as Intuitive Surgical's da Vinci Surgical System, and it remains unclear how close current humanoid systems are to meeting the precision, control and safety requirements of minimally invasive surgery. Here we present a systematic evaluation of contemporary humanoid technology for laparoscopic surgical tasks. We develop a humanoid-based laparoscopic teleoperation framework using general-purpose instruments and assess its abilities through benchtop characterization, dry-laboratory user studies spanning diverse surgical experience levels and in vivo porcine studies. Across these evaluations, we quantify technical feasibility, task performance and clinical readiness relative to established surgical platforms. Together, our study provides an evidence-based assessment of current humanoid abilities and limitations for surgical applications, highlighting both their promise and key technical challenges that must be addressed before clinical deployment.
