This paper presents a novel approach to programming soft robots using differentiable rendering. The key idea is to model the interaction between the soft robot and its environment (e.g., objects to grip, obstacles to avoid) using depth images obtained from the interior view of these objects. This eliminates the need for manually defining point correspondences and tracking landmarks, which is a common challenge in soft robotics.
The authors formulate the gripping and avoidance tasks as optimization problems, where the goal is to minimize/maximize the distance between the robot and the target object/obstacle, respectively. The distance measure is computed using differentiable rendering, which allows for gradient-based optimization of the control parameters (cable pull ratios) to achieve the desired behaviors.
The authors demonstrate the effectiveness of their approach through four experiments:
The results show that the differentiable rendering-based approach can simplify the programming of complex soft robot tasks and achieve the desired behaviors through gradient-based optimization of the control parameters.
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by Kasra Arnava... at arxiv.org 04-12-2024
https://arxiv.org/pdf/2404.07590.pdfDeeper Inquiries