Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Via Morego, 30 16163 Genova, Italy.
Neural Netw. 2012 Aug;32:109-18. doi: 10.1016/j.neunet.2012.02.011. Epub 2012 Feb 14.
This paper describes a self-protective whole body motor controller to enable life-long learning of humanoid robots. In order to reduce the damages on robots caused by physical interaction such as obstacle collision, we introduce self-protective behaviors based on the adaptive coordination of full-body global reactions and local limb reflexes. Global reactions aim at adaptive whole-body movements to prepare for harmful situations. The system incrementally learns a more effective association of the states and global reactions. Local reflexes based on a force-torque sensing function to reduce the impact load on the limbs independently of high-level motor intention. We examined the proposed method with a robot simulator in various conditions. We then applied the systems on a real humanoid robot.
本文介绍了一种自我保护的全身运动控制器,以使仿人机器人能够进行终身学习。为了减少机器人在物理交互(如障碍物碰撞)过程中受到的损害,我们引入了基于全身全局反应和局部肢体反射自适应协调的自我保护行为。全局反应旨在进行自适应的全身运动,以应对危险情况。该系统逐步学习到更有效的状态和全局反应之间的关联。基于力/扭矩感知功能的局部反射,可在不依赖高层运动意图的情况下,独立减少对肢体的冲击负载。我们在各种条件下的机器人模拟器上对所提出的方法进行了检验。然后,我们在实际的仿人机器人上应用了该系统。