Interactive Constrained Dynamics for Rigid and Deformable Objects

Following the continuous increase in computational power of consumer hardware, interactive virtual environments have been recently enriched with more and more complex deformable objects. However, many physics engines are still very limited in the way they handle interacting rigid and deformable objects. This paper proposes a constraint-based approach to real-time simulation of coupled rigid and deformable objects capable of providing two-way interactions. Similar techniques have seen widespread usage for either rigid or deformable objects, but not for the simultaneous simulation of both. By extending such approaches, we show not only how interaction is possible but also how it can be performed at real-time rates. We address contact response and also show how to implement typical constraints to enforce limitations in the degrees of freedom and to enhance the dynamical properties of deformable objects. The method is easily integrated into existing physics engines that use similar constraint solvers and is independent on the kind of deformable object paradigm chosen. The provided simulation results show that the method is fast and effective in handling contacts between rigid and deformable objects and in simulating friction and other kinds of constraints

Fast Penetration Depth Estimation for Elastic Bodies

We present a fast penetration depth estimation algorithm between deformable polyhedral objects. We assume the continuum of non-rigid models are discretized using standard techniques, such as finite element or finite difference methods. As the objects deform, the pre-computed distance fields are deformed accordingly to estimate penetration depth, allowing enforcement of non-penetration constraints between two colliding elastic bodies. This approach can automatically handle self-penetration and inter-penetration in a uniform manner. We demonstrate its effectiveness on moderately complex simulation scenes

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

Fast generation of 3D deformable moving surfaces

Dynamic surface modeling is an important subject of geometric modeling due to their extensive applications in engineering design, entertainment and medical visualization. Many deformable objects in the real world are dynamic objects as their shapes change over time. Traditional geometric modeling methods are mainly concerned with static problems, therefore unsuitable for the representation of dynamic objects. Apart from the definition of a dynamic modeling problem, another key issue is how to solve the problem. Because of the complexity of the representations, currently the finite element method or finite difference method is usually used. Their major shortcoming is the excessive computational cost, hence not ideal for applications requiring real-time performance. We propose a representation of dynamic surface modeling with a set of fourth order dynamic partial differential equations (PDEs). To solve these dynamic PDEs accurately and efficiently, we also develop an effective resolution method. This method is further extended to achieve local deformation and produce n-sided patches. It is demonstrated that this new method is almost as fast and accurate as the analytical closed form resolution method and much more efficient and accurate than the numerical methods