An example prediction sequence: The red vector located on the participant’s head is the detected face norm. The red dot indicates the gaze and table plane intersection point ψ discussed in Sec. III-B. The green skeleton shows the detected shoulder, elbow, and palm positions. For each frame (a-h) along the trajectory, the corresponding heatmap can be seen underneath. Note that at the start of the trajectory, the model is unsure of the human’s placement intent. As more and more of trajectory is realized, a prediction with increasing confidence can be seen being made.
Preemptive Motion Planning
for Human-to-Robot Indirect Placement Handovers
for Human-to-Robot Indirect Placement Handovers
Andrew Choi,
Mohammad Khalid Jawed, and
Jungseock Joo
University of California, Los Angeles
IEEE International Conference on Robotics and Automation (ICRA), 2022
Abstract
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-pickup. 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.
ICRA Presentation
Video (Robot Perspective)
Video (Third-person Perspective)