4,396 research outputs found
Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation
Accurate and robust object state estimation enables successful object
manipulation. Visual sensing is widely used to estimate object poses. However,
in a cluttered scene or in a tight workspace, the robot's end-effector often
occludes the object from the visual sensor. The robot then loses visual
feedback and must fall back on open-loop execution.
In this paper, we integrate both tactile and visual input using a framework
for solving the SLAM problem, incremental smoothing and mapping (iSAM), to
provide a fast and flexible solution. Visual sensing provides global pose
information but is noisy in general, whereas contact sensing is local, but its
measurements are more accurate relative to the end-effector. By combining them,
we aim to exploit their advantages and overcome their limitations. We explore
the technique in the context of a pusher-slider system. We adapt iSAM's
measurement cost and motion cost to the pushing scenario, and use an
instrumented setup to evaluate the estimation quality with different object
shapes, on different surface materials, and under different contact modes
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
We present a differentiable dynamics solver that is able to handle frictional
contact for rigid and deformable objects within a unified framework. 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. We combine this new contact model with fully-implicit time
integration to obtain a robust and efficient dynamics solver that is
analytically differentiable. In conjunction with adjoint sensitivity analysis,
our formulation enables gradient-based optimization with adaptive trade-offs
between simulation accuracy and smoothness of objective function landscapes. We
thoroughly analyse our approach on a set of simulation examples involving rigid
bodies, visco-elastic materials, and coupled multi-body systems. We furthermore
showcase applications of our differentiable simulator to parameter estimation
for deformable objects, motion planning for robotic manipulation, trajectory
optimization for compliant walking robots, as well as efficient self-supervised
learning of control policies.Comment: Moritz Geilinger and David Hahn contributed equally to this wor
Differentiable Stripe Patterns for Inverse Design of Structured Surfaces
Stripe patterns are ubiquitous in nature and everyday life. While the
synthesis of these patterns has been thoroughly studied in the literature,
their potential to control the mechanics of structured materials remains
largely unexplored. In this work, we introduce Differentiable Stripe Patterns
-- a computational approach for automated design of physical surfaces
structured with stripe-shaped bi-material distributions. Our method builds on
the work by Knoppel and colleagues for generating globally-continuous and
equally-spaced stripe patterns. To unlock the full potential of this design
space, we propose a gradient-based optimization tool to automatically compute
stripe patterns that best approximate macromechanical performance goals.
Specifically, we propose a computational model that combines solid shell finite
elements with XFEM for accurate and fully-differentiable modeling of elastic
bi-material surfaces. To resolve non-uniqueness problems in the original
method, we furthermore propose a robust formulation that yields unique and
differentiable stripe patterns. %Finally, we introduce design space
regularizers to avoid numerical singularities and improve stripe neatness We
combine these components with equilibrium state derivatives into an end-to-end
differentiable pipeline that enables inverse design of mechanical stripe
patterns. We demonstrate our method on a diverse set of examples that
illustrate the potential of stripe patterns as a design space for structured
materials. Our simulation results are experimentally validated on physical
prototypes.Comment: 14 page
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
We present a novel loss formulation for efficient learning of complex
dynamics from governing physics, typically described by partial differential
equations (PDEs), using physics-informed neural networks (PINNs). In our
experiments, existing versions of PINNs are seen to learn poorly in many
problems, especially for complex geometries, as it becomes increasingly
difficult to establish appropriate sampling strategy at the near boundary
region. Overly dense sampling can adversely impede training convergence if the
local gradient behaviors are too complex to be adequately modelled by PINNs. On
the other hand, if the samples are too sparse, existing PINNs tend to overfit
the near boundary region, leading to incorrect solution. To prevent such
issues, we propose a new Boundary Connectivity (BCXN) loss function which
provides linear local structure approximation (LSA) to the gradient behaviors
at the boundary for PINN. Our BCXN-loss implicitly imposes local structure
during training, thus facilitating fast physics-informed learning across entire
problem domains with order of magnitude sparser training samples. This LSA-PINN
method shows a few orders of magnitude smaller errors than existing methods in
terms of the standard L2-norm metric, while using dramatically fewer training
samples and iterations. Our proposed LSA-PINN does not pose any requirement on
the differentiable property of the networks, and we demonstrate its benefits
and ease of implementation on both multi-layer perceptron and convolutional
neural network versions as commonly used in current PINN literature.Comment: 11 pages, 7 figure
A distributed optimization framework for localization and formation control: applications to vision-based measurements
Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures
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