Toward the control of attention in a dynamically dexterous robot

In the recent successful effort to achieve the spatial two-juggle - batting two freely falling balls into independent stable periodic vertical orbits by repeated impacts with a three degree of freedom robot arm, the authors have found it necessary to introduce a dynamical window manager into their real-time stereo vision. This paper describes these necessary enhancements to the original vision system and then proposes a more formal account of how such a feedback based sensor might be understood to work. Further experimentation will be required to determine the extent to which the analytical model explains (and might thus be used as a tool to improve) the performance of the system presently working in the laboratory

Safety and Guaranteed Stability Through Embedded Energy-Aware Actuators

Safety is essential for robots in unknown environments, especially when there is physical Human-Robot Interaction (pHRI). Control over energy, or passivity, is an effective safety mechanism. However, when the control algorithm is implemented in a discrete-time computer, computation and communication delays readily lead to loss of passivity and to instability. In this paper, a way to make the actuators aware of the energy that they inject into the system is presented. Passivity and stability are then always guaranteed, even in situations of total communication loss. These Embedded Energy-Aware Actuators are a model-free passivity and safety layer that make complex robotic systems dependable, well-behaved and safe. The proposed method is validated in simulation and experiments

Enhanced robotic hand-eye coordination inspired from human-like behavioral patterns

Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method: Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: “Stop-to-Fixate”, is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot’s learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object