ContactNets: Learning Discontinuous Contact Dynamics with Smooth,
Implicit Representations
- URL: http://arxiv.org/abs/2009.11193v2
- Date: Sun, 1 Nov 2020 06:45:56 GMT
- Title: ContactNets: Learning Discontinuous Contact Dynamics with Smooth,
Implicit Representations
- Authors: Samuel Pfrommer and Mathew Halm and Michael Posa
- Abstract summary: Our method learns parameterizations of inter-body signed distance and contact-frame Jacobians.
Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real-world data.
- Score: 4.8986598953553555
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Common methods for learning robot dynamics assume motion is continuous,
causing unrealistic model predictions for systems undergoing discontinuous
impact and stiction behavior. In this work, we resolve this conflict with a
smooth, implicit encoding of the structure inherent to contact-induced
discontinuities. Our method, ContactNets, learns parameterizations of
inter-body signed distance and contact-frame Jacobians, a representation that
is compatible with many simulation, control, and planning environments for
robotics. We furthermore circumvent the need to differentiate through stiff or
non-smooth dynamics with a novel loss function inspired by the principles of
complementarity and maximum dissipation. Our method can predict realistic
impact, non-penetration, and stiction when trained on 60 seconds of real-world
data.
Related papers
- Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems [49.11170948406405]
State-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets.
We propose a method to estimate the physical parameters of any known, continuous governing equation from single videos.
arXiv Detail & Related papers (2024-10-02T09:44:54Z) - RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing [38.97168020979433]
We introduce an approach that combines visual and tactile sensing for robotic manipulation by learning a neural, tactile-informed dynamics model.
Our proposed framework, RoboPack, employs a recurrent graph neural network to estimate object states.
We demonstrate our approach on a real robot equipped with a compliant Soft-Bubble tactile sensor on non-prehensile manipulation and dense packing tasks.
arXiv Detail & Related papers (2024-07-01T16:08:37Z) - Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning.
We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures.
We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
arXiv Detail & Related papers (2024-01-22T18:58:22Z) - DiAReL: Reinforcement Learning with Disturbance Awareness for Robust
Sim2Real Policy Transfer in Robot Control [0.0]
Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions.
We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms.
arXiv Detail & Related papers (2023-06-15T10:11:38Z) - VIRT: Improving Representation-based Models for Text Matching through
Virtual Interaction [50.986371459817256]
We propose a novel textitVirtual InteRacTion mechanism, termed as VIRT, to enable full and deep interaction modeling in representation-based models.
VIRT asks representation-based encoders to conduct virtual interactions to mimic the behaviors as interaction-based models do.
arXiv Detail & Related papers (2021-12-08T09:49:28Z) - Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform.
We produce a closed-loop controller to reactively push objects in a continuous action space.
We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
arXiv Detail & Related papers (2021-11-15T18:50:04Z) - Dynamic Modeling of Hand-Object Interactions via Tactile Sensing [133.52375730875696]
In this work, we employ a high-resolution tactile glove to perform four different interactive activities on a diversified set of objects.
We build our model on a cross-modal learning framework and generate the labels using a visual processing pipeline to supervise the tactile model.
This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing.
arXiv Detail & Related papers (2021-09-09T16:04:14Z) - Learning Reactive and Predictive Differentiable Controllers for
Switching Linear Dynamical Models [7.653542219337937]
We present a framework for learning composite dynamical behaviors from expert demonstrations.
We learn a switching linear dynamical model with contacts encoded in switching conditions as a close approximation of our system dynamics.
We then use discrete-time LQR as the differentiable policy class for data-efficient learning of control to develop a control strategy.
arXiv Detail & Related papers (2021-03-26T04:40:24Z) - Learning Contact Dynamics using Physically Structured Neural Networks [81.73947303886753]
We use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects.
We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations.
Our results indicate that an idealised form of touch feedback is a key component of making this learning problem tractable.
arXiv Detail & Related papers (2021-02-22T17:33:51Z) - A Differentiable Contact Model to Extend Lagrangian and Hamiltonian
Neural Networks for Modeling Hybrid Dynamics [10.019335078365705]
We introduce a differentiable contact model, which can capture contact mechanics, both frictionless and frictional, as well as both elastic and inelastic.
We demonstrate this framework on a series of challenging 2D and 3D physical systems with different coefficients of restitution and friction.
arXiv Detail & Related papers (2021-02-12T22:02:41Z) - ADD: Analytically Differentiable Dynamics for Multi-Body Systems with
Frictional Contact [26.408218913234872]
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects.
Through a principled mollification of normal and tangential contact forces, our method circumvents the main difficulties inherent to the non-smooth nature of frictional contact.
arXiv Detail & Related papers (2020-07-02T09:51:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.