Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object. This capability is defined as ‘haptic glance’ in cognitive neuroscience. Most of the existing 3D recognition models were developed based on dense 3D data. Nonetheless, in many real-life use cases, where robots are used to collect 3D data by haptic exploration, only a limited number of 3D points could be collected. In this study, we thus focus on solving the intractable problem of how to obtain cognitively representative 3D key-points of a target object with limited interactions between the robot and the object. A novel reinforcement learning based framework is proposed, where the haptic exploration procedure (the agent iteratively predicts the next position for the robot to explore) is optimized simultaneously with the objective 3D recognition with actively collected 3D points. As the model is rewarded only when the 3D object is accurately recognized, it is driven to find the sparse yet efficient haptic-perceptua13D representation of the object. Experimental results show that our proposed model outperforms the state of the art models.

In this paper, we present the dynamic modeling of a 3D-hybrid underwater eel-like robot using recursive algorithms based on the Newton-Euler equations. The robot is composed of a sequence of parallel modules connected in series. The algorithm gives the head accelerations and the joint torques as a function of the Cartesian positions, velocities and accelerations of the platform of the modules. The proposed algorithm can be considered as a generalization of the recursive Newton-Euler inverse dynamic algorithm of serial manipulators with fixed base. The proposed algorithm is easy to implement and to simulate whatever the number of degrees of freedom of the robot. An example with 12 parallel modules is presented.

This paper presents the dynamic modeling of a 3-D-serial underwater eel-like robot using recursive algorithms based on the Newton--Euler equations. Both direct and inverse models are treated in the paper. The inverse dynamic model algorithm gives the head acceleration and the joint torques as a function of the joint positions, velocities, and accelerations. The direct dynamic model gives the head and joint accelerations as a function of the joint positions, velocities, and input torques. The proposed algorithms can be considered as a generalization of the recursive Newton--Euler dynamic algorithms of serial manipulators with fixed base. The algorithms are easy to implement and to simulate whatever the number of degrees of freedom of the robot. An example with 12 spherical joints is presented. The fluid forces have been taken into account using a simple model based on Morison's model.

In this paper, we present the dynamic modeling of a 3D-serial underwater eel-like robot using recursive algorithm based on the Newton-Euler equations. The algorithm gives the head accelerations and the joint torques as a function of the joint positions, velocities and accelerations. The proposed algorithm can be considered as a generalization of the recursive Newton-Euler inverse dynamic algorithm of serial manipulators with fixed base. The proposed algorithm is easy to implement and to simulate whatever the number of degrees of freedom of the robot. Simulation results with a 12-spherical joint structure are presented.