RSS 2026现场直击｜从双臂协同到水下灵巧，机器人操作迈入「少样本时代」

TL;DR - A report from RSS 2026 surveys nine robotics papers showing manipulation systems shifting from data-intensive training toward few-shot learning and stronger generalization. The featured methods reuse human demonstrations, bridge simulation and real-world domains, and explicitly model contact or distribution shifts.

• BiDemoSyn synthesizes thousands of dual-arm trajectories from one demonstration, while DexImit and TactAlign transfer skills from human video or unpaired tactile data.
• Surgical and tactile systems achieve robust manipulation with limited real demonstrations by using mixture-of-experts architectures, simulation pretraining, and multimodal contact representations.
• UMI-Underwater trains from land-based demonstrations and uses depth representations plus self-supervised trials to enable underwater manipulation without underwater teleoperation.
• SID reports roughly 90% success under out-of-distribution initial conditions with only two demonstrations by steering the robot back toward familiar states at inference time.