most citedSupervised Mixture-of-Experts for Surgical Grasping and Retraction

2 citations · 3 across the 6 of their papers we have counts for

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cs.RO2026

Language-Conditioned Behavioral Cloning for Autonomous Endoscope Guidance

Ariel Rodriguez, Lorenzo Mazza, Ortrun Hellig +3

Automated endoscope guidance reduces the need for continuous manual camera control during minimally invasive surgery. However, the desired camera motion depends on the surgeon's im…

cs.RO2026★ 1 cited

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Open-H-Embodiment Consortium, :, Nigel Nelson +213

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…

cs.RO2026

An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery

Ariel Rodriguez, Lorenzo Mazza, Martin Lelis +4

Autonomous robot-assisted surgery demands reliable, high-precision platforms that strictly adhere to the safety and kinematic constraints of minimally invasive procedures. Existing…

cs.RO2026

LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning

Ariel Rodriguez, Chenpan Li, Lorenzo Mazza +5

Imitation learning enables robots to acquire manipulation skills from demonstrations, yet deploying a policy across tasks with heterogeneous dynamics remains challenging, as models…

cs.RO2026★ 2 cited

Supervised Mixture-of-Experts for Surgical Grasping and Retraction

Lorenzo Mazza, Ariel Rodriguez, Rayan Younis +6

Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due to data scarcity, constrained workspace…