Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers
This addresses efficiency in collaborative human-robot teams, but it is incremental as it builds on existing handover methods with a specific focus on indirect placements.
The paper tackles the problem of idle time in human-to-robot indirect placement handovers by developing a prediction-planning pipeline that uses gaze and gestures to preemptively predict human intent and plan robot motion in real-time, resulting in reduced wait times as demonstrated in a case study.
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.