We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins.Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory.We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype.
The seminal work by Witkin and Kass @cite_50 generated motion trajectories by optimizing physical constraints and animator controls at key frames, a well-known space-time constraints framework for animation. With proper motion data, the space-time optimization produces realistic articulated motions for bipedal or multilegged characters through different physical properties @cite_46 @cite_41 @cite_22 @cite_49 @cite_77 @cite_86 @cite_59 @cite_79 @cite_15 . It can be used to transform motion capture data into physically plausible motions @cite_44 .
@cite_22 @cite_15 @cite_41 @cite_44 @cite_77 @cite_79 @cite_49 @cite_50 @cite_59 @cite_86 @cite_46
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We introduce a novel algorithm for transforming character animation sequences that preserves essential physical properties of the motion. By using the spacetime constraints dynamics formulation our algorithm maintains realism of the original motion sequence without sacrificing full user control of the editing process. In contrast to most physically based animation techniques that synthesize motion from scratch, we take the approach of motion transformationas the underlying paradigm for generating computer animations. In doing so, we combine the expressive richness of an input animation sequence with the controllability of spacetime optimization to create a wide range of realistic character animations. The spacetime dynamics formulation also allows editing of intuitive, high-level motion concepts such as the time and placement of footprints, length and mass of various extremities, number of body joints and gravity. Our algorithm is well suited for the reuse of highly-detailed captured motion animations. In addition, we describe a new methodology for mapping a motion between characters with drastically different numbers of degrees of freedom. We use this method to reduce the complexity of the spacetime optimization problems. Furthermore, our approach provides a paradigm for controlling complex dynamic and kinematic systems with simpler ones. We present a novel hybrid representation for character animation from 4D Performance Capture (4DPC) data which combines skelet al control with surface motion graphs. 4DPC data are temporally aligned 3D mesh sequence reconstructions of the dynamic surface shape and associated appearance from multiple-view video. The hybrid representation supports the production of novel surface sequences which satisfy constraints from user-specified key-frames or a target skelet al motion. Motion graph path optimisation concatenates fragments of 4DPC data to satisfy the constraints while maintaining plausible surface motion at transitions between sequences. Space-time editing of the mesh sequence using a learned part-based Laplacian surface deformation model is performed to match the target skelet al motion and transition between sequences. The approach is quantitatively evaluated for three 4DPC datasets with a variety of clothing styles. Results for key-frame animation demonstrate production of novel sequences that satisfy constraints on timing and position of less than 1p of the sequence duration and path length. Evaluation of motion-capture-driven animation over a corpus of 130 sequences shows that the synthesised motion accurately matches the target skelet al motion. The combination of skelet al control with the surface motion graph extends the range and style of motion which can be produced while maintaining the natural dynamics of shape and appearance from the captured performance. This paper presents a method for generating dynamically feasible, keyframe-interpolating motions for robots undergoing contact, such as in legged locomotion and manipulation. The first stage generates a twice-differentiable interpolating path that obeys kinematic contact constraints up to a user-specified tolerance. The second stage optimizes speeds along the path to minimize time while satisfying dynamic constraints. The method supports velocity, acceleration, and torque constraints, and polyhedral contact friction constraints at an arbitrary number of contact points. The method is numerically stable, and empirical running time is weakly linear in the number of degrees of freedom and polynomial in the time-domain grid resolution. Experiments demonstrate that full-body motions for robots with 100 degrees of freedom and dozens of contact points are calculated in seconds. Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.