Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of observations. Our focus is on estimating these properties based on observations of scenes with different sets of interacting objects. We propose an approach that exploits the structure of the reward function by modeling the reward for each observation separately and using only the parameters of the objects in that scene as inputs. The resulting lower-dimensional models generalize better over the parameter space, which in turn results in a faster optimization. To speed up the optimization process further, and reduce the number of simulation runs needed to find good parameter values, we also propose partial evaluations of the reward function, wherein the selected parameters are only evaluated on a subset of real world evaluations. The approach was successfully evaluated on a set of scenes with a wide range of object interactions, and we showed that our method can effectively perform incremental learning without resetting the rewards of the gathered observations.

机器人需要从观察中估计物体的材料和动态特性，本研究提出了基于贝叶斯优化的方法来识别物体的材料属性参数，通过对不同场景下互动物体的观察进行估计，利用奖励函数的结构，仅使用场景中对象的参数作为输入，得到更好的广义模型来加速优化过程，还通过部分评估奖励函数来减少仿真运行次数，实验证明该方法能够有效进行增量学习而不重置奖励。

使用Sum-GP-UCB估计相互作用物体的材料属性