A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods

Accurate frictional contact is critical in simulating the assembly of rod-like structures in the practical world, such as knots, hairs, flagella, and more. Due to their high geometric nonlinearity and elasticity, rod-on-rod contact remains a challenging problem tackled by researchers in both computational mechanics and computer graphics. Typically, frictional contact is regarded as constraints for the equations of motions of a system. Such constraints are often computed independently at every time step in a dynamic simulation, thus slowing down the simulation and possibly introducing numerical convergence issues. This paper proposes a fully implicit penalty-based frictional contact method, Implicit Contact Model (IMC), that efficiently and robustly captures accurate frictional contact responses. We showcase our algorithm's performance in achieving visually realistic results for the challenging and novel contact scenario of flagella bundling in fluid medium, a significant phenomenon in biology that motivates novel engineering applications in soft robotics. In addition to this, we offer a side-by-side comparison with Incremental Potential Contact (IPC), a state-of-the-art contact handling algorithm. We show that IMC possesses comparable performance to IPC while converging at a faster rate.Comment: * Equal contribution. A video summarizing this work is available on YouTube: https://youtu.be/g0rlCFfWJ8

Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding

Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as imitation learning, have been used to address deformable material manipulation. These approaches lack generality and often suffer critical failure from a simple switch of material, geometric, and/or environmental (e.g., friction) properties. This article tackles a fundamental but difficult deformable manipulation task: forming a predefined fold in paper with only a single manipulator. A data-driven framework combining physically-accurate simulation and machine learning is used to train a deep neural network capable of predicting the external forces induced on the manipulated paper given a grasp position. We frame the problem using scaling analysis, resulting in a control framework robust against material and geometric changes. Path planning is then carried out over the generated "neural force manifold" to produce robot manipulation trajectories optimized to prevent sliding, with offline trajectory generation finishing 15$\times$ faster than previous physics-based folding methods. The inference speed of the trained model enables the incorporation of real-time visual feedback to achieve closed-loop sensorimotor control. Real-world experiments demonstrate that our framework can greatly improve robotic manipulation performance compared to state-of-the-art folding strategies, even when manipulating paper objects of various materials and shapes.Comment: Supplementary video is available on YouTube: https://youtu.be/k0nexYGy-P

mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals

Robotic manipulation of deformable materials is a challenging task that often requires realtime visual feedback. This is especially true for deformable linear objects (DLOs) or "rods", whose slender and flexible structures make proper tracking and detection nontrivial. To address this challenge, we present mBEST, a robust algorithm for the realtime detection of DLOs that is capable of producing an ordered pixel sequence of each DLO's centerline along with segmentation masks. Our algorithm obtains a binary mask of the DLOs and then thins it to produce a skeleton pixel representation. After refining the skeleton to ensure topological correctness, the pixels are traversed to generate paths along each unique DLO. At the core of our algorithm, we postulate that intersections can be robustly handled by choosing the combination of paths that minimizes the cumulative bending energy of the DLO(s). We show that this simple and intuitive formulation outperforms the state-of-the-art methods for detecting DLOs with large numbers of sporadic crossings and curvatures with high variance. Furthermore, our method achieves a significant performance improvement of approximately 40 FPS compared to the 15 FPS of prior algorithms, which enables realtime applications.Comment: YouTube video: https://youtu.be/q84I9i0DOK

Sim2Real Neural Controllers for Physics-based Robotic Deployment of Deformable Linear Objects

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task -- accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.Comment: YouTube video: https://youtu.be/OSD6dhOgyMA?feature=share

Exploration of the Synergy between Computational Mechanics and Robotics for Slender Structures

Slender structures, widely found from natural environments (e.g., tendrils) to engineering applications (e.g., flexible electronics), frequently experience geometrically nonlinear deformations and substantial topological changes when exposed to simple boundary conditions or modest external stimuli. On one hand, the nonlinear dynamics of slender structures present considerable challenges for the automated manipulation of these structures by robots. On the other hand, the automated interactions between robots and such structures also open up opportunities to enhance our understanding of the mechanics governing slender structures.This dissertation focuses on the synergy between computational mechanics and robotics for the manipulation and study of slender structures. Specifically, it delves into discrete differential geometry (DDG)-based simulations, an emerging field in computational mechanics, to develop a comprehensive sim2Real manipulation framework for generating task-oriented deformable manipulation strategies. Moreover, we conduct automated experiments to gain valuable insights into the behavior of slender structures. Our contributions can be categorized into three main areas:First, we develop a penalty-energy-based method and combine it with Kirchoff rod's theory to simulate rod assemblies with frictional contact responses. Our simulation method is validated, demonstrating its robustness, accuracy, and efficiency across diverse scenarios. These scenarios include modeling flagella bundling, a significant biological phenomenon for bacterial navigation, as well as tying knots. These numerical validations underscore the potential of our approach as a significant step toward the ultimate goal of a computational framework for sim2real manipulation tasks. We then combine our numerical framework with desktop experiments to investigate the mechanics of various types of knots.Second, we combine DDG-based simulations, scaling analysis, and machine learning to develop a sim2Real framework for various deformable manipulation tasks, including paper folding and the deployment of deformable linear objects onto rigid substrates. Our sim2Real framework harnesses the precision of physical simulations, the rapid inference capabilities of neural networks, and the enhanced adaptability conferred by scaling analysis. This synergy yields robust, accurate, and efficient solutions for these manipulation tasks. In the paper folding task, a physics-informed model is learned using scaled simulation data, enabling the creation of a model predictive control system for precise paper folding. We validate the effectiveness of this physics-based approach through extensive robotic experiments. In addition, we construct a physics-informed manipulation policy within the same framework for the deployment task. This policy proves to be robust, accurate, and efficient in controlling the shape of various deformable linear objects during deployments. Furthermore, we demonstrate the potential of this deployment scheme in various engineering applications including cable management and knot tying.Finally, we delve into the application of automation science to explore the nonlinear mechanics of slender structures. Traditional experimental platforms (e.g., optical platforms) struggle to systematically capture the numerous boundary conditions and corresponding equilibriums of slender structures. To address this challenge, we've designed a robotic system for automated experiments. This system allows us to investigate one of the fundamental problems in solid mechanics: the buckling of an elastic rod with a helical centerline. We answer this problem with a combination of theoretical analysis, numerical simulation, and automated robotic experiments. Then, significant advances are made in understanding this phenomenon, uncovering different buckling types within this system, including continuous buckling and snap buckling. Given the distinct behaviors of these two types of buckling, our exploration is particularly meaningful in demonstrating how various buckling can be triggered within a single system. Our automated robotic experiments highlight the potential of robotic technology in advancing our understanding of mechanics through intelligent interactions with the physical world

Static analysis of elastic cable structures under mechanical load using discrete catenary theory

In this paper, the nonlinear mechanical response of elastic cable structures under mechanical load is studied based on the discrete catenary theory. A cable net is discretized into multiple nodes and edges in our numerical approach, which is followed by an analytical formulation of the elastic energy and the associated Hessian matrix to realize the dynamic simulation. A fully implicit framework is proposed based on the discrete differential geometry (DDG) theory. The equilibrium configuration of a target object is derived by adding damping force into the system, known as the dynamic relaxation method. The mechanical response of a single suspended cable is investigated and compared with the analytical solution for cross-validation. A more intricate scenario is further discussed in detail, where a structure consisting of multiple slender cables is connected through joints. Utilizing the robustness and efficiency of our discrete numerical framework, a systematic parameter sweep is performed to quantify the force displacement relationships of nets with the different number of cables and different directions of fibers. Finally, an empirical scaling law is provided to account for the rigidity of elastic cable net in terms of its geometric properties, material characteristics, component numbers, and cable orientations. Our results would provide new insight in revealing the connections between flexible structures and tensegrity structures, and could motivate innovative designs in both mechanical and civil engineered equipment

EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels

Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time