  Surgical automation can improve the accessibility and consistency of life
saving procedures. Most surgeries require separating layers of tissue to access
the surgical site, and suturing to reattach incisions. These tasks involve
deformable manipulation to safely identify and alter tissue attachment
(boundary) topology. Due to poor visual acuity and frequent occlusions,
surgeons tend to carefully manipulate the tissue in ways that enable inference
of the tissue's attachment points without causing unsafe tearing. In a similar
fashion, we propose JIGGLE, a framework for estimation and interactive sensing
of unknown boundary parameters in deformable surgical environments. This
framework has two key components: (1) a probabilistic estimation to identify
the current attachment points, achieved by integrating a differentiable
soft-body simulator with an extended Kalman filter (EKF), and (2) an
optimization-based active control pipeline that generates actions to maximize
information gain of the tissue attachments, while simultaneously minimizing
safety costs. The robustness of our estimation approach is demonstrated through
experiments with real animal tissue, where we infer sutured attachment points
using stereo endoscope observations. We also demonstrate the capabilities of
our method in handling complex topological changes such as cutting and
suturing.
