@misc{4b73e3a1-8996-44dd-b1f0-790b256c0d77,
  abstract     = {{Robot-to-human handovers are fundamental for collaborative robots, particularly in surgical contexts where instruments must be delivered accurately without regrasping.<br/><br/>We investigate the role of hand representation in learning precise robot-to-human handover behaviors in complex contexts such as surgery. Using a dataset of 180 teleoperated demonstrations, an image-based Action Chunking with Transformers (ACT) policy is compared to a variant conditioned on explicit 3D hand pose information extracted from visual observations.<br/><br/>Our findings highlight the importance of representation in close human-robot interaction tasks and suggest that explicit hand pose information is critical for learning precise and usable robot-to-human handovers in surgical settings.}},
  author       = {{Ertürk, Esranur and Stenmark, Maj and Topp, Elin Anna}},
  language     = {{eng}},
  title        = {{Getting a Grip: Learning Precise Robot-To-Human Handovers with Hand Pose Conditioning}},
  url          = {{https://ras.papercept.net/conferences/conferences/ROMAN26/program/ROMAN26_ContentListWeb_3.html}},
  year         = {{2026}},
}