9,185 research outputs found
A Massively-Parallel 3D Simulator for Soft and Hybrid Robots
Simulation is an important step in robotics for creating control policies and
testing various physical parameters. Soft robotics is a field that presents
unique physical challenges for simulating its subjects due to the nonlinearity
of deformable material components along with other innovative, and often
complex, physical properties. Because of the computational cost of simulating
soft and heterogeneous objects with traditional techniques, rigid robotics
simulators are not well suited to simulating soft robots. Thus, many engineers
must build their own one-off simulators tailored to their system, or use
existing simulators with reduced performance. In order to facilitate the
development of this exciting technology, this work presents an
interactive-speed, accurate, and versatile simulator for a variety of types of
soft robots. Cronos, our open-source 3D simulation engine, parallelizes a
mass-spring model for ultra-fast performance on both deformable and rigid
objects. Our approach is applicable to a wide array of nonlinear material
configurations, including high deformability, volumetric actuation, or
heterogenous stiffness. This versatility provides the ability to mix materials
and geometric components freely within a single robot simulation. By exploiting
the flexibility and scalability of nonlinear Hookean mass-spring systems, this
framework simulates soft and rigid objects via a highly parallel model for near
real-time speed. We describe an efficient GPU CUDA implementation, which we
demonstrate to achieve computation of over 1 billion elements per second on
consumer-grade GPU cards. Dynamic physical accuracy of the system is validated
by comparing results to Euler-Bernoulli beam theory, natural frequency
predictions, and empirical data of a soft structure under large deformation
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Real-life control tasks involve matters of various substances---rigid or soft
bodies, liquid, gas---each with distinct physical behaviors. This poses
challenges to traditional rigid-body physics engines. Particle-based simulators
have been developed to model the dynamics of these complex scenes; however,
relying on approximation techniques, their simulation often deviates from
real-world physics, especially in the long term. In this paper, we propose to
learn a particle-based simulator for complex control tasks. Combining learning
with particle-based systems brings in two major benefits: first, the learned
simulator, just like other particle-based systems, acts widely on objects of
different materials; second, the particle-based representation poses strong
inductive bias for learning: particles of the same type have the same dynamics
within. This enables the model to quickly adapt to new environments of unknown
dynamics within a few observations. We demonstrate robots achieving complex
manipulation tasks using the learned simulator, such as manipulating fluids and
deformable foam, with experiments both in simulation and in the real world. Our
study helps lay the foundation for robot learning of dynamic scenes with
particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video:
https://www.youtube.com/watch?v=FrPpP7aW3L
Creating gameplay mechanics with deformable characters
This paper presents how soft body simulation can create deformable characters and physics-based game mechanics that result in a more varied gameplay experience. A framework was implemented that allows the creation of a fully deformable soft body character within a games application where the simulation model properties could be altered at runtime to create gameplay mechanics based on varying the deformation of the character. The simulation model was augmented to allow appropriate methods of player control that complemented the character design and its ability to deform. It was found that while the implementation of deformation-based mechanics created a more varied gameplay experience, the underlying simulation model allowed for a limited amount of deformation before becoming unstable. The ffectiveness of the framework is demonstrated by the resulting mechanics that are not possible through the use of previous methods
- …