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  • 提供个人资料
  • 布朗机器人小组致力于实现机器人和自治系统,这些系统是追求人类努力的有效合作者。为此,我们探讨了人类机器人互动,机器人学习,机器人感知,自主控制,灵活的操纵和游戏开发方面的问题。我们对这些问题的解决方案具有应用程序,包括人类机器人团队,人形机器人机器人,机器人足球和神经假体。
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  • Projects

    • 概率运动跟踪的物理模拟

    • We explore the use of full-body 3D physical simulation for human kinematic tracking from monocular and multi-view video sequences within the Bayesian filtering framework. Towards greater physical plausibility, we consider a human's motion to be generated by a ``feedback control loop'', where Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. The result is more faithful modeling of human-environment interactions, such as ground contacts, resulting from collisions and the human's motor control.
    • 多机枪马尔可夫随机字段

    • 我们将马尔可夫随机字段(MRF)作为概率数学模型,用于统一多机器人协调的方法或更具体地说是分布式操作选择。我们描述了多机器人协调的现有方法如何符合基于MRF的模型以及它们在概念上如何统一的方法。此外,我们提供对多机器人MRF的信念传播,作为分布式机器人动作选择的替代方法。
    • Learning Robot Soccer from Demonstration

    • 我们寻求使用户能够教授个人机器人任意任务,从而使机器人在不明确编程的情况下更好地满足用户的需求和需求。机器人从演示中学习是一种非常适合这种范式的方法,因为机器人从对任务本身的观察中从新环境中学习新任务。许多当前的机器人学习算法需要存在可以组合以执行所需任务的基本行为的存在。但是,长期存在世界上存在的机器人可能会耗尽此基础设置。特别是,可以要求机器人执行未知任务,其内置行为可能不合适。
    • Sparse Control for Manipulation

    • Human control of high degree-of-freedom robotic systems is often difficult due to the overwhelming number of variables that need to be specified. Instead, we propose the use of sparse subspaces embedded within the pose space of a robotic system. Driven by human motion, we addressed this sparse control problem by uncovering 2D subspaces that allow cursor control, or eventually decoding of neural activity, to drive a robotic hand. Considering the problems in previous work related to noise in pose graph construction and motion capture, we introduced a method for denoising neighborhood graphs for embedding hand motion into 2D spaces. Such spaces allow for control of high-DOF systems using 2D interfaces such as cursor control via mouse or decoding of neural activity. We present results demonstrating our approach to interactive sparse control for successful power grasping and precision grasping using a 13 DOF robot hand.
    • 操纵歧管

    • 我们探索了使用多种学习技术从远程操作的类人动物操纵任务中发现结构的使用。NASA的人形机器人Robonaut的数据在通过四项工具操纵任务进行远程处理时被记录下来。我们表明,一种算法,时空的ISOMAP,能够揭示行为结构,而行为结构可能很难通过其他降低技术(主要成分分析,多维缩放标度和ISOMAP)找到。
    • Human Pose and Action Recognition for Interactive Robots

    • There is currently a division between real-world human performance and the decision making of socially interactive robots. Specifically, the decision making of robots needs to have information about the decision making of its human collaborators. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards crossing this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies.
    • Sketch-based Mesh Animation

    • 我们提出的方法阐述,及功率ng meshes, in particular facial meshes, through a 2D sketching interface. Our method establishes an interface between 3D meshes and 2D sketching with the inference of reference and target curves. Reference curves allow for user selection of features on a mesh and their manipulation to match a target curve. Our articulation system uses these curves to specify the deformations of a character rig, forming a coordinate space of mesh poses. Given such a coordinate space, our posing system uses reference and target curves to find the optimal pose of the mesh with respect to the sketch input. We present results demonstrating the efficacy of our method for mesh articulation, mesh posing with articulations generated in both Maya and our sketch-based system, and mesh animation using human features from video. Through our method, we aim to both provide novice-accessible articulation and posing mesh interfaces and rapid prototyping of complex deformations for more experienced users.
    • World Space Motion Control for Humanoids

    • Dynamo(动态运动捕获)是在动态虚拟世界中控制动画字符的一种方法。利用现有方法,字符同时进行了身体模拟和驱动以执行运动运动运动(来自MOCAP或其他来源)。连续模拟允许字符比在Ragdoll模拟和纯运动捕获之间交替的方法更现实地交互。

      发电机的新颖贡献是增加稳定性的世界空间扭矩,而根源较弱的根源则是合理平衡的。从传统的父骨参考框架到世界空间参考框架促进关节目标角度,可以使角色设置和维护姿势,从而对动态相互作用进行健全。它还在不明确混合的动作之间产生身体上合理的过渡...

    • SMURV机器人平台

    • The SmURV platform (Small Universal Robotics Vehicle) is a comparatively cheap and easy to assemble robotics platform for educational, research and hobby purposes. Essentially, the platform consists of an iRobot Create and a small computer mounted on top of it. Due to this very simple design, using components available at any larger electronics store, we are able to build an autonomous robot in a very short amount of time and can focus on the real interesting part: Namely, Making the robot do something exciting! In the following paragraphs we show what parts are needed to build a SmURV, how much they approximately cost and how they have to be assembled. Further, we offer one particular (out of many possible) software solutions to control a SmURV and to write software for it.
    • Robot Learning and Gaming

    • Towards accessible teaching of robots from demonstration, we have developed a mixed-reality distributed multi-player robotic gaming environment. Our goal is to provide robot learning researchers with an means to collect large corpora of data representative of human decision making. Robot control by an human operator (or teleoperation) is cast in a video game style interface to leverage the ubiquity and popularity of games while minimizing tedium in robot training.
    • 在机器人传感器数据中发现类别

    • 我们通过数据驱动的方法来解决机器人感知的符号接地问题,从而从机器人传感器数据中得出类别。与基于模型的方法不同,在传感器读数和环境的特征(拐角,门等)之间寻求人类直观的对应关系,我们的方法从原始数据本身学习了内在类别(或自然种类)。我们使用ISOMAP非线性尺寸降低近似歧管传感器数据,并使用模型识别技术应用贝叶斯聚类(高斯混合模型)来发现类别(或类型)。我们通过学习具有不同传感器方式的各种室内和室外环境中的试验中的感觉种类来展示我们的方法。然后,学习种类用于对新的传感器数据进行分类(样本外读数)。我们提出的结果表明,在非线性低维嵌入中使用混合模型对传感器数据进行分类的一致性。