6 research outputs found
Coarse-graining collective skyrmion dynamics in confined geometries
Magnetic skyrmions are magnetic quasi-particles with enhanced stability and
different manipulation mechanisms using external fields and currents making
them promising candidates for future applications for instance in neuromorphic
computing. Recently, several measurements and simulations have shown that
thermally activated skyrmions in confined geometries, as they are necessary for
device applications, arrange themselves predominantly based on commensurability
effects. In this simulational study, based on the Thiele model, we investigate
the enhanced dynamics and degenerate non-equilibrium steady state of a system
in which the intrinsic skyrmion-skyrmion and skyrmion-boundary interaction
compete with thermal fluctuations as well as current-induced spin-orbit
torques. The investigated system is a triangular-shaped confinement geometry
hosting four skyrmions, where we inject spin-polarized currents between two
corners of the structure. We coarse-grain the skyrmion states in the system to
analyze the intricacies of skyrmion arrangements of the skyrmion ensemble. In
the context of neuromorphic computing, such methods address the key challenge
of optimizing read-out positions in confined geometries and form the basis to
understand collective skyrmion dynamics in systems with competing interactions
on different scales.Comment: 11 pages, 4 figure
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation
The ability to leverage heterogeneous robotic experience from different
robots and tasks to quickly master novel skills and embodiments has the
potential to transform robot learning. Inspired by recent advances in
foundation models for vision and language, we propose a foundation agent for
robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned
decision transformer capable of consuming multi-embodiment action-labelled
visual experience. This data spans a large repertoire of motor control skills
from simulated and real robotic arms with varying sets of observations and
actions. With RoboCat, we demonstrate the ability to generalise to new tasks
and robots, both zero-shot as well as through adaptation using only 100--1000
examples for the target task. We also show how a trained model itself can be
used to generate data for subsequent training iterations, thus providing a
basic building block for an autonomous improvement loop. We investigate the
agent's capabilities, with large-scale evaluations both in simulation and on
three different real robot embodiments. We find that as we grow and diversify
its training data, RoboCat not only shows signs of cross-task transfer, but
also becomes more efficient at adapting to new tasks