16,022 research outputs found
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
In recent years, an increasing amount of work has focused on differentiable
physics simulation and has produced a set of open source projects such as Tiny
Differentiable Simulator, Nimble Physics, diffTaichi, Brax, Warp, Dojo and
DiffCoSim. By making physics simulations end-to-end differentiable, we can
perform gradient-based optimization and learning tasks. A majority of
differentiable simulators consider collisions and contacts between objects, but
they use different contact models for differentiability. In this paper, we
overview four kinds of differentiable contact formulations - linear
complementarity problems (LCP), convex optimization models, compliant models
and position-based dynamics (PBD). We analyze and compare the gradients
calculated by these models and show that the gradients are not always correct.
We also demonstrate their ability to learn an optimal control strategy by
comparing the learned strategies with the optimal strategy in an analytical
form. The codebase to reproduce the experiment results is available at
https://github.com/DesmondZhong/diff_sim_grads.Comment: 2nd AI4Science Workshop at ICML 202
Propagation Networks for Model-Based Control Under Partial Observation
There has been an increasing interest in learning dynamics simulators for
model-based control. Compared with off-the-shelf physics engines, a learnable
simulator can quickly adapt to unseen objects, scenes, and tasks. However,
existing models like interaction networks only work for fully observable
systems; they also only consider pairwise interactions within a single time
step, both restricting their use in practical systems. We introduce Propagation
Networks (PropNet), a differentiable, learnable dynamics model that handles
partially observable scenarios and enables instantaneous propagation of signals
beyond pairwise interactions. Experiments show that our propagation networks
not only outperform current learnable physics engines in forward simulation,
but also achieve superior performance on various control tasks. Compared with
existing model-free deep reinforcement learning algorithms, model-based control
with propagation networks is more accurate, efficient, and generalizable to
new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu
Video: https://youtu.be/ZAxHXegkz4
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
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
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