As technology advances, the need for safe, efficient, and collaborative
human-robot-teams has become increasingly important. One of the most
fundamental collaborative tasks in any setting is the object handover.
Human-to-robot handovers can take either of two approaches: (1) direct
hand-to-hand or (2) indirect hand-to-placement-to-pick-up. The latter approach
ensures minimal contact between the human and robot but can also result in
increased idle time due to having to wait for the object to first be placed
down on a surface. To minimize such idle time, the robot must preemptively
predict the human intent of where the object will be placed. Furthermore, for
the robot to preemptively act in any sort of productive manner, predictions and
motion planning must occur in real-time. We introduce a novel
prediction-planning pipeline that allows the robot to preemptively move towards
the human agent's intended placement location using gaze and gestures as model
inputs. In this paper, we investigate the performance and drawbacks of our
early intent predictor-planner as well as the practical benefits of using such
a pipeline through a human-robot case study.

该研究介绍了一种预测和规划机制，该机制通过目光和手势作为模型输入来预测人类意图，并实时移动向人类指定的位置，以实现人机协作的高效安全的物体递交。

人机间间接握手的先发制人运动规划

Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers

Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.

本文介绍了一种基于学习算法的动态物体移交方法，通过与人类交互学习物体移交的潜在报酬，使机器人能够自然地适应人体运动的动态，并通过上下文策略搜索来建立问题模型。