From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness
This work addresses the challenge of robots adapting to human carefulness in manipulation tasks, though it is incremental as it focuses on a specific capability within human-robot interaction.
The study tackled the problem of enabling a humanoid robot to infer online whether a human partner is careful when moving an object, using vision alone, and achieved up to 81.3% accuracy with a low-resolution camera.
When manipulating objects, humans finely adapt their motions to the characteristics of what they are handling. Thus, an attentive observer can foresee hidden properties of the manipulated object, such as its weight, temperature, and even whether it requires special care in manipulation. This study is a step towards endowing a humanoid robot with this last capability. Specifically, we study how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. Only for short movements without obstacles, carefulness recognition was insufficient. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.