Bimanual handovers are crucial for transferring large, deformable or delicate
objects. This paper proposes a framework for generating kinematically
constrained human-like bimanual robot motions to ensure seamless and natural
robot-to-human object handovers. We use a Hidden Semi-Markov Model (HSMM) to
reactively generate suitable response trajectories for a robot based on the
observed human partner's motion. The trajectories are adapted with task space
constraints to ensure accurate handovers. Results from a pilot study show that
our approach is perceived as more human--like compared to a baseline Inverse
Kinematics approach.

提出了一个框架来生成运动学约束的类人双手机器人运动，以确保人与机器之间物体的顺畅和自然的交接。通过使用隐藏半马尔可夫模型（HSMM）根据观察到的人的动作生成适当的响应轨迹，通过任务空间约束调整轨迹以确保准确的递交。通过一项试点研究的结果表明，与基线逆运动学方法相比，我们的方法被认为更类似于人类。