Sheldon Andrews

Interactive Scanning of Haptic Textures and Surface Compliance

In modern computer graphics, 3D scanning is common practise for the acquisition of the geometry of objects. However, in addition to geometric models, physical models of interaction behaviour are required for the realistic representation of objects in arbitrary environments. In this paper, we introduce a hand-held scanning approach for the acquisition of physical surface texture (roughness) of real- world 3D objects. Our system utilizes a low-cost mobile touch probe and image-based tracking to allow an operator to interactively scan a real-world object and generate estimates of surface texture and compliance. These scans can be integrated into the 3D scanning pipeline, just as colour imagery can be included into the pipeline for the acquisition of visual texture. We demonstrate that the acquired surface properties are of sufficient quality to allow for haptic display of the scanned object.

HAMLAT: A HAML-Based Authoring Tool for Haptic Application Development

Haptic applications have received enormous attention in the last decade. Nonetheless, the diversity of haptic interfaces, virtual environment modeling, and rendering algorithms have made the development of hapto-visual applications a tedious and time consuming task that requires significant programming skills. To tackle this problem, we present a HAML-based Authoring Tool (HAMLAT), an authoring tool based on the HAML description language that allows users to render the graphics and haptics of a virtual environment with no programming skills. The modeler is able to export the developed application in a standard HAML description format. The proposed system comprises three components: the authoring tool (HAMLAT), the HAML engine, and the HAML player. The tool is implemented by extending the 3D Blender modeling platform to support haptic interaction. The demonstrated tool proves the simplicity and efficiency of prototyping haptic applications for non-programmer developers or artists.

Measurement-Based Modeling of Contact Forces and Textures for Haptic Rendering

Haptic texture represents the fine-grained attributes of an objects surface and is related to physical characteristics such as roughness and stiffness. We introduce an interactive and mobile scanning system for the acquisition and synthesis of haptic textures that consists of a visually tracked handheld touch probe. The most novel aspect of our work is an estimation method for the contact stiffness of an object based solely on the acceleration and forces measured during stroking of its surface with the handheld probe. We establish an experimental relationship between the estimated stiffness and the contact stiffness observed during compression. We also measure the height-displacement profile of an objects surface enabling us to generate haptic textures. We show an example of mapping the textures on to a coarse surface mesh obtained with an image-based technique, but the textures may also be combined with coarse surface meshes obtained by manual modeling.

Technical Section: Goal directed multi-finger manipulation: Control policies and analysis

We present a method for one-handed, task-based manipulation of objects. Our approach uses a mid-level, multi-phase approach to organize the problem into three phases. This provides an appropriate control strategy for each phase and results in cyclic finger motions that, together, accomplish the task. The exact trajectory of the object is never specified since the goal is defined by the final orientation and position of the object. All motion is physically based and guided by a control policy that is learned through a series of offline simulations. We also discuss practical considerations for our learning method. Variations in the synthesized motions are possible by tuning a scalarized multi-objective optimization. We demonstrate our method with two manipulation tasks, discussing the performance and limitations. Additionally, we provide an analysis of the robustness of the low-level controllers used by our framework.

Real-time Physics-based Motion Capture with Sparse Sensors

We propose a framework for real-time tracking of humans using sparse multi-modal sensor sets, including data obtained from optical markers and inertial measurement units. A small number of sensors leaves the performer unencumbered by not requiring dense coverage of the body. An inverse dynamics solver and physics-based body model are used, ensuring physical plausibility by computing joint torques and contact forces. A prior model is also used to give an improved estimate of motion of internal joints. The behaviour of our tracker is evaluated using several black box motion priors. We show that our system can track and simulate a wide range of dynamic movements including bipedal gait, ballistic movements such as jumping, and interaction with the environment. The reconstructed motion has low error and appears natural. As both the internal forces and contacts are obtained with high credibility, it is also useful for human movement analysis.

Data-driven Fingertip Appearance for Interactive Hand Simulation

Contact on a finger pad results in deformation that redistributes blood within the fingertip tissue in a manner correlated to the pressure. We build a data-driven model that relates contact information to the visible changes of the finger nail and surrounding tissue on the back of the finger tip. Our data analysis and model construction makes use of the space of hemoglobin concentrations, as opposed to an RGB color space, which permits the model to be transferred across different fingers and different people. We use principal component analysis to build a compact model which maps well to graphics hardware with an efficient fragment program implementation. We provide a validation of our model, and a demonstration of a grasping controller running in a physically based simulation, where grip strength is visible in both hand posture and the appearance of color changes at the fingertips.

Policies for Goal Directed Multi-Finger Manipulation

We present a method for one-handed task based manipulation of objects. Our approach uses a mid-level multiphase approach to break the problem into three parts, providing an appropriate control strategy for each phase and resulting in cyclic finger motions that accomplish the task. All motion is physically based, and guided by a policy computed for a particular task. The exact trajectory is never specified as the goal of our different tasks are concerned with the final orientation and position of the object. The offline simulations used to learn the policy are effective solutions for the task, but an important aspect of our work is that the policy is general enough to be used online in real time. We present two manipulation tasks and discuss their performance along with limitations.

Geometric Stiffness for Real-time Constrained Multibody Dynamics

This paper focuses on the stable and efficient simulation of articulated rigid body systems for real‐time applications. Specifically, we focus on the use of geometric stiffness which can dramatically increase simulation stability. We examine several numerical problems with the inclusion of geometric stiffness in the equations of motion, as proposed by previous work, and address these issues by introducing a novel method for efficiently building the linear system. This offers improved tractability and numerical efficiency. Furthermore, geometric stiffness tends to significantly dissipate kinetic energy. We propose an adaptive damping scheme, inspired by the geometric stiffness, that uses a stability criterion based on the numerical integrator to determine the amount of non‐constitutive damping required to stabilize the simulation. With this approach, not only is the dynamical behavior better preserved, but the simulation remains stable for mass ratios of 1,000,000‐to‐1 at time steps up to 0.1 s. We present a number of challenging scenarios to demonstrate that our method improves efficiency, and that it increases stability by orders of magnitude compared to previous work.

Task-based design of cable-driven articulated mechanisms

We present a framework for the automatic design of articulated cable-driven mechanisms performing push andpick-and-place tasks. Provided an initial topology and task specification, our system optimizes the morphology and cable mechanisms such that the resulting mechanism can perform the desired task successfully. Optimizing for multiple tasks and multiple cables simultaneously is possible with our framework. At the core of our approach is an optimization algorithm that analyzes the kinematics of the design to evaluate the mechanisms ability to perform the task. Dynamical attributes, such as the ability to produce forces at the end effector, are also considered. Furthermore, this paper presents a novel approach for fast inverse kinematics using cable-driven mechanisms, which is used in the morphology optimization process. Several examples of mechanisms designed using our framework are presented. We also present results of physics based simulation, and evaluate 3D printed versions of an example mechanism.

Blended Linear Models for Reduced Compliant Mechanical Systems

We present a method for the simulation of compliant, articulated structures using a plausible approximate model that focuses on modeling endpoint interaction. We approximate the structures behavior about a reference configuration, resulting in a first order reduced compliant system, or FORK-1S. Several levels of approximation are available depending on which parts and surfaces we would like to have interactive contact forces, allowing various levels of detail to be selected. Our approach is fast and computation of the full structures state may be parallelized. Furthermore, we present a method for reducing error by combining multiple FORK-1S models at different linearization points, through twist blending and matrix interpolation. Our approach is suitable for stiff, articulate grippers, such as those used in robotic simulation, or physics-based characters under static proportional derivative control. We demonstrate that simulations with our method can deal with kinematic chains and loops with non-uniform stiffness across joints, and that it produces plausible effects due to stiffness, damping, and inertia.