Teaching nullspace constraints in physical human-robot interaction using Reservoir Computing
Teaching nullspace constraints in physical human-robot interaction using Reservoir Computing
复制标题
使用储层计算教授物理人机交互中的零空间约束
DOI：
10.1109/icra.2012.6225170
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Jochen J. Steil
中科院分区：
文献类型：
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作者：
Arne Nordmann;C. Emmerich;Stefan Rüther;Andre Lemme;S. Wrede;Jochen J. Steil
当前机器人学研究的一个主要目标是使机器人成为与人类高效协作并适应不断变化的环境或工作流程的同事。为了使冗馀机器人的快速重构成为可能，我们提出了一种在相干系统中利用柔顺机器人的物理交互能力进行数据驱动和无模型学习的方法。没有特定机器人知识的用户可以在与兼容机器人的物理交互中执行该任务，例如，由于环境的变化而重新配置工作单元。为了快速有效地学习各自的零空间约束，采用了水库神经网络。它嵌入到系统的运动控制器中，因此允许在任务空间执行任意运动。我们描述了系统的训练、探索和控制体系结构，并对KUKA轻量级机器人进行了评估。结果表明，所学习的模型在给定约束条件下以足够的精度解决了冗馀消解问题，并且即使在工作空间的未训练区域也能泛化生成有效的关节空间轨迹。
A major goal of current robotics research is to enable robots to become co-workers that collaborate with humans efficiently and adapt to changing environments or workflows. We present an approach utilizing the physical interaction capabilities of compliant robots with data-driven and model-free learning in a coherent system in order to make fast reconfiguration of redundant robots feasible. Users with no particular robotics knowledge can perform this task in physical interaction with the compliant robot, for example to reconfigure a work cell due to changes in the environment. For fast and efficient learning of the respective null-space constraints, a reservoir neural network is employed. It is embedded in the motion controller of the system, hence allowing for execution of arbitrary motions in task space. We describe the training, exploration and the control architecture of the systems as well as present an evaluation on the KUKA Light-Weight Robot. Our results show that the learned model solves the redundancy resolution problem under the given constraints with sufficient accuracy and generalizes to generate valid joint-space trajectories even in untrained areas of the workspace.