To safely and efficiently extract an object from the clutter, this paper presents a bimanual manipulation planner in which one hand of the robot is used to slide the target object out of the clutter while the other hand is used to support the surrounding objects to prevent the clutter from collapsing. Our method uses a neural network to predict the physical phenomena of the clutter when the target object is moved. We generate the most efficient action based on the Monte Carlo tree search. The grasping and sliding actions are planned to minimize the number of motion sequences to pick the target object. In addition, the object to be supported is determined to minimize the position change of surrounding objects. Experiments with a real bimanual robot confirmed that the robot could retrieve the target object, reducing the total number of motion sequences and improving safety.

Modern manufacturing strategy requires the robotic infrastructure to be able to adapt to new products or to accomplish new tasks quickly. In order to respond to this demand, teaching a robot to realize a task by demonstration has regained popularity in recent years, especially for dual-arm or humanoid robots. One of the main issues using this method is to adapt the captured motion from the human demonstration to the robot's specific kinematics and control. In this paper we present a method where the motion and grasping adaptation is tackled during the capture. We demonstrate the validity of this method with an experiment where a humanoid robot realizes an assembly previously demonstrated by a user wearing a Head Mounted Display (HMD) performing an assembly task in a virtual environment.

This paper presents a framework to embody a user (e.g. disabled persons) into a humanoid robot controlled by means of brain-computer interfaces (BCI). With our framework, the robot can interact with the environment, or assist its user. The low frequency and accuracy of the BCI commands is compensated by vision tools, such as objects recognition and mapping techniques, as well as shared-control approaches. As a result, the proposed framework offers intuitive, safe, and accurate robot navigation towards an object or a person. The generic aspect of the framework is demonstrated by two complex experiments, where the user controls the robot to serve him a drink, and to raise his own arm.

We present an experiment we carried out to determine the influence of the video feedback on the braincomputer interface performance of a system we designed to steer a humanoid robot. The interface is based on the wellknown steady-state visually evoked potentials and the stimuli are integrated into the live feedback from the robot's embedded camera. Five users controlled the HRP-2 humanoid in an experiment designed to measure the performance of the intentions' recognition system. A novel approach in the training phase is also experimented to understand and compensate performance loss due to the dynamic nature of the video feedback of the robot during walking motions. It results that this feedback induces a performance loss; we propose an effective solution to overcome this problem. The detailed results of these experiments are reported in this paper and we discuss the possible causes of performance loss under such conditions.