Bottom-Up Synchronisation of Spontaneous Quadruped Walking Gaits for Cooperative Predator Modeling

用于合作捕食者建模的自发四足行走步态的自下而上同步

• 批准号: 2080115
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2080115
• 关键词: Bottom; Up; Synchronisation; Spontaneous; Quadruped

A quadruped is an agent with four limbs which it uses for terrestrial motion. The term quadruped means "four feet". Emulating quadruped motion in animals such as horses, cheetahs, and dogs has been the focus of research since the mid-90s, but a solution to this problem is elusive. The difficulty with emulating quadruped motion is the range of leg behaviours, called gaits, that a quadruped animal can express. The list of gaits for a quadruped include, walk, trot, pace, canter, transverse gallop, rotary gallop, bound, and pronk. To obtain such a wide variety of motion, hand-built models are designed for each gait type, and the transition between gaits is structured as a state machine. This model fails when the quadruped has to perform behaviours that are not represented by the state machine. Alternatively, some work uses biologically-motivated coupled oscillators to model the motion; this still requires tuning of the coupling parameters for each of the gaits. A new idea which hasn't gained much traction yet is inspired by the self-organising properties of metronomes on a surface with feedback. Each leg is an oscillating metronome, with no interaction with the other legs except via local feedback from ground forces and the undulations of the spine. This simple model naturally transitions between the quadruped gaits as the angular velocity of the legs is increased. My research is to explore the extend to which this model can be used for realistic simulation of quadruped behaviour. To do this, the model must be extended beyond the experimental phase by enabling it to perform actions such as turning and jumping. The model will then be used to build wolf pack simulations of wolves hunting prey via reinforcement learning. Firstly, the wolf and the prey use the self-organising quadruped model to move through the environment. This provides a solution to one of the unsolved problems in physics-based reinforcement learning which is to learn physically-realistic locomotion behaviour. Next, the brain of the wolves are represented by a neural network which is trained via reinforcement learning techniques. To make the problem interesting, a lone wolf cannot capture the prey because the prey is faster than the wolf. Wolves must therefore learn to coordinate their behaviours so that together they can capture the prey. What is interesting about this problem is that it addresses a relatively new area of learning tasks which focus on agents learning to cooperate via low-bandwidth communication; that is, a wolf does not know all the information about another wolf's decision, therefore a form of language must be learned to transmit the information between wolves. Some of the applications for this research include:1. Streamlined design of predator-prey animations in movies, video games, or animal simulations.2. Gaining insight into how language is used in pack animals to perform tasks beyond the capability of any single animal in the pack.3. How morphological properties of animals (that is, the self-organising quadruped model described earlier) are essential for learning realistic animal behaviours, instead of the traditional top-down learning methods employed in some complex models which don't produce realistic motion.

四足动物是一种有四肢的媒介，它用来在陆地上运动。四足动物这个词的意思是“四只脚”。自90年代中期以来，在马、猎豹和狗等动物中模拟四足动物的运动一直是研究的焦点，但这个问题的解决方案是难以捉摸的。模仿四足动物运动的困难在于四足动物可以表达的腿行为的范围，称为步态。四足动物的步态包括：步行、快步、步速、慢跑、横向奔驰、旋转奔驰、跳跃和蹬踏。为了获得这样的各种各样的运动，手工构建的模型被设计为每种步态类型，步态之间的过渡被构造为一个状态机。当四足动物必须执行状态机没有表示的行为时，该模型失败。或者，一些工作使用生物激励的耦合振荡器来模拟运动;这仍然需要调整每个步态的耦合参数。一个新的想法还没有获得太多的牵引力，灵感来自于表面反馈的节拍器的自组织特性。每一条腿都是一个振荡的节拍器，除了通过地面力量和脊柱起伏的局部反馈外，与其他腿没有相互作用。这个简单的模型随着腿的角速度的增加而在四足动物步态之间自然地过渡。我的研究是探索这个模型可以用于四足动物行为的真实模拟的程度。要做到这一点，模型必须通过使其能够执行转向和跳跃等动作来扩展到实验阶段之外。然后，该模型将用于通过强化学习构建狼群狩猎猎物的狼群模拟。首先，狼和猎物使用自组织四足动物模型在环境中移动。这为基于物理的强化学习中未解决的问题之一提供了解决方案，即学习物理现实的运动行为。接下来，狼的大脑由通过强化学习技术训练的神经网络表示。为了使这个问题更有趣，一只孤独的狼不能捕获猎物，因为猎物比狼快。因此，狼必须学会协调它们的行为，这样它们才能一起捕获猎物。这个问题的有趣之处在于，它解决了一个相对较新的学习任务领域，该领域专注于通过低带宽通信学习合作的代理;也就是说，狼不知道另一只狼的决定的所有信息，因此必须学习一种语言形式来传输狼之间的信息。本研究的一些应用包括：1。电影、视频游戏或动物模拟中捕食者-猎物动画的流线型设计。2.深入了解驮畜如何使用语言来完成超出驮畜能力的任务。动物的形态学特性（即前面描述的自组织四足动物模型）对于学习真实的动物行为是至关重要的，而不是传统的自上而下的学习方法，这些方法在一些不产生真实运动的复杂模型中使用。