This paper builds up the skill of impact aware non prehensile manipulation through a hitting motion of a redundant robot arm by allowing it to come in contact with the environment with the appropriate link according to the requirements of the hitting task. In tasks where directional effective inertia of a robot is important at the contact point, it is useful to understand inertia at different links, so as to select the appropriate link. Hitting with those links allows us to manipulate a wider range of object masses since the robot effective inertia is different at different links. We propose a learning based methodology for selecting a hitting link based on the hitting task specifications, impact posture generation for the robot and an automated generation of desired directional inertia values throughout the hitting motion.

In this article, we propose a metric called hitting flux, which is used in the motion generation and controls for a robot manipulator to interact with the environment through a hitting or a striking motion. Given the task of placing a known object outside of the workspace of the robot, the robot needs to come in contact with it at a nonzero relative speed. The configuration of the robot and the speed at contact matter because they affect the motion of the object. The physical quantity called hitting flux depends on the robot's configuration, the robot speed, and the properties of the environment. An approach to achieve the desired directional preimpact flux for the robot through a combination of a dynamical system for motion generation and a control system that regulates the directional inertia of the robot is presented. Furthermore, a quadratic program formulation for achieving a desired inertia matrix at a desired position while following a motion plan constrained to the robot limits is presented. The system is tested for different scenarios in simulation showing the repeatability of the procedure and in real scenarios with KUKA LBR iiwa 7 robot.

One can estimate the velocity and acceleration of robot manipulators by utilizing nonlinear observers. This involves combining inertial measurement units (IMUs) with the motor encoders of the robot through a model-based sensor fusion technique. This approach is lightweight, versatile (suitable for a wide range of trajectories and applications), and straightforward to implement. In order to further improve the estimation accuracy while running the system, we propose to adapt the noise information in this paper. This would automatically reduce the system vulnerability to imperfect modelings and sensor changes. Moreover, viable strategies to maintain the system stability are introduced. Finally, we thoroughly evaluate the overall framework with a seven DoF robot manipulator whose links are equipped with IMUs.

This paper proposes a manipulation scheme based on learning the motion of objects after being hit by a robotic end-effector. This allows for the object to be positioned at a desired location outside the physical workspace of the robot. An estimate of the object dynamics under friction and collisions is learnt and used to predict the desired hitting parameters (speed and direction), given the initial and desired location of the object. Based on the obtained hitting parameters, the desired pre-impact velocity of the end-effector is generated using a stable dynamical system. The performance of the proposed DS is validated in simulation and and is used to learn a model for hitting using real robot. The approach is tested on real robot with a KUKA LBR IIWA robot.