We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as moving overhanging vines and branches for navigation in dense vegetation. The underlying deformable tree geometry is encapsulated as coarse spring abstractions executed on parallel, non-differentiable simulators. The implicit statistical model defined by the simulator, reference trajectories obtained by actively probing the ground truth, and the Bayesian formalism, together guide the spring parameter posterior density estimation. Our non-parametric inference algorithm, based on Stein Variational Gradient Descent, incorporates biologically motivated assumptions into the inference process as neural network driven learnt joint priors; moreover, it leverages the finite difference scheme for gradient approximations. Real and simulated experiments confirm that our model can predict deformation trajectories, quantify the estimation uncertainty, and it can perform better when base-lined against other inference algorithms, particularly from the Monte Carlo family. The model displays strong robustness properties in the presence of heteroscedastic sensor noise; furthermore, it can generalise to unseen grasp locations.

翻译：我们提出了一种基于模拟驱动的逆推方法，用于建模操控下树木枝干的动力学。学习枝干动力学并具备操控柔性植被的能力，有助于处理易被遮挡的任务，例如在茂密枝叶中采摘水果，以及在茂密植被中移动悬垂藤蔓和枝干以进行导航。底层可变形树木几何结构被封装为在并行、不可微模拟器上执行的粗粒度弹簧抽象模型。由模拟器定义的隐式统计模型、通过主动探测真实场景获得的参考轨迹以及贝叶斯形式化方法共同引导了弹簧参数后验密度估计。我们的非参数推理算法基于斯坦变分梯度下降，将生物启发性假设作为神经网络驱动的学习联合先验融入推理过程；同时，它利用有限差分格式进行梯度近似。真实实验与仿真实验均证实，该模型能预测形变轨迹、量化估计不确定性，且相较于其他推理算法（尤其是蒙特卡洛族方法）表现更优。该模型在异方差传感器噪声下展现出强鲁棒性，并能泛化至未见过的抓取位置。