IEEE Trans Cybern. 2016 Jul;46(7):1691-703. doi: 10.1109/TCYB.2015.2454334. Epub 2015 Aug 26.
We investigate the modeling of inverse dynamics without prior kinematic information for holonomic rigid-body robots. Despite success in compensating robot dynamics and friction, general inverse dynamics models are nontrivial. Rigid-body models are restrictive or inefficient; learning-based models are generalizable yet require large training data. The structured kernels address the dilemma by embedding the robot dynamics in reproducing kernel Hilbert space. The proposed kernels autonomously converge to rigid-body models but require fewer samples; with a semi-parametric framework that incorporates additional parametric basis for friction, the structured kernels can efficiently model general rigid-body robots. We tested the proposed scheme in simulations and experiments; the models that consider the structure of function space are more accurate.
我们研究了完整刚体机器人在没有先验运动学信息的情况下的逆动力学建模问题。尽管在补偿机器人动力学和摩擦力方面取得了成功，但通用逆动力学模型并不简单。刚体模型具有局限性或效率低下；基于学习的模型具有通用性，但需要大量的训练数据。结构化内核通过将机器人动力学嵌入再生核希尔伯特空间来解决这个困境。所提出的内核自主收敛到刚体模型，但需要更少的样本；通过一个半参数框架，该框架包含了用于摩擦力的附加参数基础，结构化内核可以有效地对一般刚体机器人进行建模。我们在模拟和实验中测试了所提出的方案；考虑函数空间结构的模型更准确。
