Learning to Simulate Tree-Branch Dynamics for Manipulation

TL;DR

This paper introduces a simulation-driven inverse inference method to model and manipulate the dynamics of deformable tree branches, aiding robotic tasks like fruit picking and navigation through dense vegetation.

Contribution

It presents a novel Bayesian inference approach using Stein Variational Gradient Descent to estimate spring parameters for deformable tree models, incorporating neural network priors and non-differentiable simulation.

Findings

Accurately predicts deformation trajectories.

Quantifies estimation uncertainty effectively.

Outperforms Monte Carlo-based inference algorithms.

Abstract

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.