Shared control can help in teleoperated object manipulation by assisting with
the execution of the user's intention. To this end, robust and prompt intention
estimation is needed, which relies on behavioral observations. Here, an
intention estimation framework is presented, which uses natural gaze and motion
features to predict the current action and the target object. The system is
trained and tested in a simulated environment with pick and place sequences
produced in a relatively cluttered scene and with both hands, with possible
hand-over to the other hand. Validation is conducted across different users and
hands, achieving good accuracy and earliness of prediction. An analysis of the
predictive power of single features shows the predominance of the grasping
trigger and the gaze features in the early identification of the current
action. In the current framework, the same probabilistic model can be used for
the two hands working in parallel and independently, while a rule-based model
is proposed to identify the resulting bimanual action. Finally, limitations and
perspectives of this approach to more complex, full-bimanual manipulations are
discussed.

使用自然注视和运动特征来预测当前动作和目标对象的一个意图估计框架，可在较拥挤的场景中在单手或双手操作时进行训练和测试，并提出了一个基于规则的模型来识别双手操作。