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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.

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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 realworld 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.

Figure 1: A heavy vehicle attached to a nearly inextensible cable is dropped from the end of a crane. The simulation involves very large mass ratios, a heterogeneous collection of joints, and remains stable at a time step of 1/60 s. Cable dynamics are well preserved by our adaptive damping method. AbstractThis 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.