Junlei Hu

Robotic exploration of unknown soft objects presents significant challenges for autonomous systems due to unpredictable deformations and shape changes during manipulation. To address this, we propose a framework that integrates topology-aware 3D reconstruction with a topology-guided motion planner, enabling the discovery and reconstruction of previously hidden or concave regions. This topology-aware 3D reconstruction employs a novel representation of deformable objects by combining Cylinder Čech Complexes with point clouds, enabling rapid tracking of significant topology changes and detection of non-manifold boundaries.The topology analysis and canonical reconstruction guide motion planning by optimising grasp points and planning trajectories to reveal previously unseen surfaces through two actions: turning over and stretching. We validated our algorithm through simulations and experiments using the \textitda Vinci Research Kit, demonstrating successful exploration with two or three manipulators. We showed it can fully explore surfaces of two everyday objects, a beanie and a rubber glove, and two cadaveric organs, a liver and a colon, within seven manipulations. Our method achieved a 45.6% improvement in 3D reconstruction accuracy compared to state-of-the-art point-cloud-based methods while also demonstrating the capability to detect and fix non-manifold geometry.

Robotic deformable object manipulation faces critical challenges in surgical applications due to under-actuation, unpredictable tissue deformation, and limited intraoperative visibility. Traditional model-free methods often encounter unstable Jacobians caused by ill-conditioned observations, while physics-based models require precise tissue parameters and volumetric meshes, limiting their practicality in real surgeries. We propose a wavelet-Boundary Element Method (BEM) framework that encodes deformations from partial 3D shape feedback using multiscale wavelet descriptors. Coupling wavelets with BEM allows computation of the nonlinear motion-deformation mapping via boundary integrals, eliminating the need for volumetric meshes and enabling real-time control under occluded views. We validate our approach in simulation and on the da Vinci Research Kit (dVRK) using phantom organs and ex vivo animal tissue, achieving millimetre-level accuracy with contour, curve, and surface feedback. Comparative studies with Fourier-based and model-free methods show superior stability in dexterous tissue manipulation, addressing ill-conditioning through spatial-frequency localization. This framework enhances robotic manipulation in unstructured surgical environments, providing robust control and accurate tissue interaction despite partial observability. Our results demonstrate its potential to improve precision and safety in minimally invasive procedures.

Robotic manipulation of 3-D soft objects remains challenging in the industrial and medical fields. Various methods based on mechanical modeling, data-driven approaches or explicit feature tracking have been proposed. A unifying disadvantage of these methods is the high computational cost of simultaneous imaging processing, identification of mechanical properties, and motion planning, leading to a need for less computationally intensive methods. We propose a method for autonomous robotic manipulation with 3-D surface feedback to solve these issues. First, we produce a deformation model of the manipulated object, which estimates the robots’ movements by monitoring the displacement of surface points surrounding the manipulators. Then, we develop a 6-degree-of-freedom velocity controller to manipulate the grasped object to achieve a desired shape. We validate our approach through comparative simulations with existing methods and experiments using phantom and cadaveric soft tissues with the da Vinci research kit. The results demonstrate the robustness of the technique to occlusions and various materials. Compared to state-of-the-art linear and data-driven methods, our approach is more precise by 46.5% and 15.9% and saves 55.2% and 25.7% manipulation time, respectively.