Control of movement time and sequential action through attractor dynamics : a simulation study demonstrating object interception and coordination

The timing of movements and of action sequences is difficult when on-line coupling to sensory information is a requirement. That requirement arises in most behavior-based robot architectures, in which relatively low-level and often noisy sensor input is used to initiate and steer action. We show how an attractor dynamics approach to the generation of behavior in such architectures can be extended to the timing of motor acts. We propose a two-layer architecture, in which a competitive "neural" dynamics controls the qualitative dynamics of a second, "timing" layer. At that second layer, periodic attractors generate timed movement. By activating such limit cycles over limited time intervals, discrete movements and movement sequences can be obtained. We demonstrate the approach by simulating two tasks that involve control of timing: the interception of moving objects by a simple two-degree-of-freedom robot arm and the temporal coordination of the end-effector motions of two six-degree-of-freedom robot arms.Fundação para a Ciência e a Tecnoloia (FCT

Control for throwing manipulation by one joint robot

Abstract-This paper proposes a throwing manipulation strategy for a robot with one revolute joint. The throwing manipulation enables the robot not only to manipulate the object to outside of the movable range of the robot, but also to control the position of the object arbitrarily in the vertical plane even though the robot has only one degree of freedom. In the throwing manipulation, the robot motion is dynamic and quick, and the contact state between the robot and the object changes. These make it difficult to obtain the exact model and solve its inverse problem. In addition, since the throwing manipulation requires more powerful actuators than the static manipulation, we should set the control input by taking consideration of the performance limits of the actuators. The present paper proposes the control strategy based on the iteration optimization learning to overcome the above problems and verifies its effectiveness experimentally

On the Control of a One Degree-of-Freedom Juggling Robot

International audienceThis paper is devoted to the feedback control of a one degree-of-freedom (dof) juggling robot, considered as a subclass of mechanical systems subject to a unilateral constraint. The proposed approach takes into account the whole dynamics of the system, and focuses on the design of a force input. It consists of a family of hybrid feedback control laws, that allow to stabilize the object around some desired (periodic or not) trajectory. The closed-loop behavior in presence of various disturbances is studied. Despite good robustness properties, the importance of good knowledge of the system parameters, like the restitution coefficient, is highlighted. Besides its theoretical interest concerning the control of a class of mechanical systems subject to unilateral constraints, this study has potential applications in non-prehensile manipulation, extending pushing robotic tasks to striking-and-pushing tasks

High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards

Robots that can learn in the physical world will be important to enable robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necessity of sample efficiency and safety for robot learning algorithms. In contrast to prior work which mainly focuses on the learning algorithm, we propose a learning system, that directly incorporates these requirements in the design of the policy representation, initialization, and optimization. We demonstrate that this system enables the high-speed Barrett WAM manipulator to learn juggling two balls from 56 minutes of experience with a binary reward signal and finally juggles continuously for up to 33 minutes or about 4500 repeated catches. The videos documenting the learning process and the evaluation can be found at https://sites.google.com/view/jugglingbo