Skaterbots

Abstract

Fig. 1. Robotic creatures created with our computational design system employ arbitrary arrangements of legs and wheels to locomote.We present a computation-driven approach to design optimization and motion synthesis for robotic creatures that locomote using arbitrary arrangements of legs and wheels. Through an intuitive interface, designers first create unique robots by combining different types of servomotors, 3D printable connectors, wheels and feet in a mix-and-match manner. With the resulting robot as input, a novel trajectory optimization formulation generates walking, rolling, gliding and skating motions. These motions emerge naturally based on the components used to design each individual robot. We exploit the particular structure of our formulation and make targeted simplifications to significantly accelerate the underlying numerical solver without compromising quality. This allows designers to interactively choreograph stable, physically-valid motions that are agile and compelling. We furthermore develop a suite of user-guided, semi-automatic, and fully-automatic optimization tools that enable motion-aware edits of the robot's physical structure. We demonstrate the efficacy of our design methodology by creating a diverse array of hybrid legged/wheeled mobile robots which we validate using physics simulation and through fabricated prototypes.

3D Dynamic Image Modeling Based on Machine Learning in Film and Television Animation

“…Motion capture data has high dimensionality, large amount of information, complex structure, spatiotemporal continuity and Riemannian manifold structure, all of which bring challenges to motion synthesis. Motion hybrid [12][13][14][15] is a simple and efficient motion synthesis model. Such methods first preprocess motion segments of the same type, including using the DTW algorithm to align them in time sequence, and then make each motion frame have similar spatial coordinates through linear transformation, that is, coordinate alignment.…”

Designing actuation systems for animatronic figures via globally optimal discrete search

“…Prior work has tackled design problems for robotic systems with multiple actuated degrees of freedom, typically in the context of locomotion or flight [Du et al 2016;Geilinger et al 2018;Jelisavcic et al 2017;Leger et al 1999;Megaro et al 2015]. Some of these works formulate the design task as tree or graph search problems [Ha et al 2018a;Zhao et al 2020], as we do.…”

Learning to fly

“…This body of work aims at (1) making it more efficient for experts to explore design spaces and find unprecedented solutions and (2) lowering the barrier of entry for design by casual users, enabling a new age of customization. Recent advances in the field propose new interactive techniques to design mechanical characters [Coros et al 2013], robotic creatures [Megaro et al 2015], creatures with wheels and legs [Geilinger et al 2018], and multicopters [Du et al 2016]. Our method builds upon this research and proposes a tool for designing hybrid UAVs by mixing and matching parts from an expert-designed collection [Desai et al 2017;Schulz et al 2017].…”

“…Users design the geometry of a hybrid UAV by selecting and matching parts from a component data set. This data set contains parametric components designed by experts and composition rules that constrain how the components can be assembled [Du et al 2016;Geilinger et al 2018;Schulz et al 2017]. We built a numerical simulator to reproduce the real experimental environment for UAVs that takes into account random sensor noise, variances in system identification, and delay in control signals (Section 4).…”