Topology-based Motion Synthesis

基于拓扑的运动合成

• 批准号: EP/H012338/1
• 负责人: Taku Komura
• 金额: $ 58.48万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH012338%2F1
• 关键词: Topology; based; Motion; Synthesis

One of the major drivers of research in the area of humanoid robotics is the desire to achieve motions involving close contact between robots and the environment or people, such as while carrying an injured person, handling flexible objects such as the straps of a knapsack or clothes. Currently, these applications seem beyond the ability of existing motion synthesis techniques due to the underlying computational complexity in an open-ended environment. Traditional methods for motion synthesis suffer from two major bottlenecks. Firstly, a significant amount of computation is required for collision detection and obstacle avoidance in the presence of numerous close contacts between manipulator segments and objects. Secondly, any particular computed solution can easily become invalid as the environment changes. For instance, if the robot were handling an object such as a knapsack, even small deformations of this flexible object and minor changes in object dimensions (e.g., between an empty bag and a stuffed bag) might require complete re-planning in the current way of solving the problem. Similar issues arise in the area of computer animation, where there is a need for real-time control of characters - moving away from static sequences of pre-programmed motion. Although it may seem that this world is much more contained, as it is created by an animation designer, there is in fact a strong desire to create games and simulation systems where the users get to interact with the world continually and expect the animation system to react accordingly. This calls for the same sort of advances in motion synthesis techniques as outlined above.The fundamental problem lies in the representation of the state of the world and the robot. Typically, motion is synthesizes in a complete configuration or state space represented at the level of generalized coordinates enumerating all joint angles and their 3D location/orientation with respect to some world reference frame. This implies the need for large amounts of collision checking calculations and randomized exploration in a very large search space. Moreover, it is very hard to encode higher level, semantic, specifications at this level of description as the individual values of the generalized coordinates do not tell us anything unless further calculations are carried out to ensure satisfaction of relevant constraints. This is particularly inconvenient when searching for a motion in a large database. The focus of this research is to alleviate these problems by developing methods that exploit the underlying topological structure in these problems, e.g., in the space of postures. This allows us to define a new search space where the coordinates are based on topological relationships, such as between link segments. We refer to this space in terms of 'topology coordinates'. In preliminary work, we have shown the utility of this viewpoint for efficient motion synthesis with characters that are in close contacts. We have also demonstrated that this approach is more efficient for categorizing semantically similar motions. In this project, we will develop a more general framework of such techniques that will be applicable to a large class of tasks carried out by autonomous humanoid robots and virtual animated characters. Moreover, we will implement our techniques on industrially relevant platforms, through our collaborators at Honda Research Institute Europe GmbH and Namco Bandai, Japan.

人形机器人研究领域的主要驱动力之一是实现机器人与环境或人之间密切接触的运动的愿望，例如在搬运伤员时，处理柔性物体，如背包或衣服的带子。目前，由于在开放式环境中潜在的计算复杂性，这些应用似乎超出了现有运动合成技术的能力。传统的运动合成方法有两个主要的瓶颈。首先，在机械臂部分与物体密切接触的情况下，碰撞检测和避障需要大量的计算量。其次，随着环境的变化，任何特定的计算解都很容易失效。例如，如果机器人正在处理一个像背包这样的物体，即使这个柔性物体的微小变形和物体尺寸的微小变化（例如，在一个空袋子和一个塞满的袋子之间）都可能需要以当前的解决问题的方式进行完全的重新规划。类似的问题也出现在电脑动画领域，在那里需要对角色进行实时控制——远离预先编程的静态动作序列。虽然这个世界看起来更加封闭，因为它是由动画设计师创造的，但实际上，人们强烈希望创造游戏和模拟系统，让用户不断与世界互动，并期望动画系统做出相应的反应。这需要同样的运动合成技术的进步，如上所述。最根本的问题在于对世界和机器人状态的表征。通常，运动是在一个完整的构型或状态空间中合成的，在广义坐标水平上表示，枚举所有关节角及其相对于某个世界参照系的三维位置/方向。这意味着需要在非常大的搜索空间中进行大量的碰撞检查计算和随机探索。此外，很难在这个描述级别上对更高级别的语义规范进行编码，因为广义坐标的单个值不能告诉我们任何东西，除非进行进一步的计算以确保满足相关约束。当在大型数据库中搜索运动时，这尤其不方便。本研究的重点是通过开发利用这些问题中潜在拓扑结构的方法来缓解这些问题，例如，在姿势空间中。这允许我们定义一个新的搜索空间，其中坐标基于拓扑关系，例如链路段之间的关系。我们用“拓扑坐标”来表示这个空间。在初步工作中，我们已经展示了这种观点的效用，用于与密切接触的角色进行有效的运动合成。我们还证明了这种方法对语义相似的动作进行分类更有效。在这个项目中，我们将开发一个更通用的技术框架，该框架将适用于由自主人形机器人和虚拟动画角色执行的大量任务。此外，我们将通过我们在本田研究所欧洲有限公司和日本南梦宫万代的合作者在工业相关平台上实施我们的技术。

项目成果

Belief and Truth in Hypothesised Behaviours

• DOI: 10.1016/j.artint.2016.02.004
• 发表时间: 2015-07
• 期刊: Artif. Intell.
• 作者: Stefano V. Albrecht;J. Crandall;S. Ramamoorthy

Learning in non-stationary MDPs as transfer learning

非平稳 MDP 中的学习作为迁移学习

• 发表时间: 2013
• 作者: Hassan Mahmud M M

Relationship descriptors for interactive motion adaptation

• DOI: 10.1145/2485895.2485905
• 发表时间: 2013-07
• 作者: R. Al-Ashqar;T. Komura;Myung Geol Choi

A Game-theoretic Model and Best-response Learning Method for Ad Hoc Coordination in Multiagent System

多智能体系统中临时协调的博弈论模型和最佳响应学习方法

• 发表时间: 2013
• 作者: Albrecht S V

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks

利用因果关系进行动态贝叶斯网络中的选择性置信过滤

• 发表时间: 2016
• 期刊: Journal of Artificial Intelligence Research
• 影响因子: 5
• 作者: Albrecht S.V.