1,583 research outputs found
An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems
Non-linear dynamical systems represent a compact, flexible, and robust tool
for reactive motion generation. The effectiveness of dynamical systems relies
on their ability to accurately represent stable motions. Several approaches
have been proposed to learn stable and accurate motions from demonstration.
Some approaches work by separating accuracy and stability into two learning
problems, which increases the number of open parameters and the overall
training time. Alternative solutions exploit single-step learning but restrict
the applicability to one regression technique. This paper presents a
single-step approach to learn stable and accurate motions that work with any
regression technique. The approach makes energy considerations on the learned
dynamics to stabilize the system at run-time while introducing small deviations
from the demonstrated motion. Since the initial value of the energy injected
into the system affects the reproduction accuracy, it is estimated from
training data using an efficient procedure. Experiments on a real robot and a
comparison on a public benchmark shows the effectiveness of the proposed
approach.Comment: Accepted at the International Conference on Robotics and Automation
202
On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds
The generation of energy-efficient and dynamic-aware robot motions that
satisfy constraints such as joint limits, self-collisions, and collisions with
the environment remains a challenge. In this context, Riemannian geometry
offers promising solutions by identifying robot motions with geodesics on the
so-called configuration space manifold. While this manifold naturally considers
the intrinsic robot dynamics, constraints such as joint limits,
self-collisions, and collisions with the environment remain overlooked. In this
paper, we propose a modification of the Riemannian metric of the configuration
space manifold allowing for the generation of robot motions as geodesics that
efficiently avoid given regions. We introduce a class of Riemannian metrics
based on barrier functions that guarantee strict region avoidance by
systematically generating accelerations away from no-go regions in joint and
task space. We evaluate the proposed Riemannian metric to generate
energy-efficient, dynamic-aware, and collision-free motions of a humanoid robot
as geodesics and sequences thereof.Comment: Accepted for publication in IEEE/RSJ Intl. Conf. on Intelligent
Robots and Systems (IROS) 2023. 8 pages, 7 figures, accompanying video at
https://youtu.be/qT43XgYOlU
Learning Stable Robotic Skills on Riemannian Manifolds
In this paper, we propose an approach to learn stable dynamical systems
evolving on Riemannian manifolds. The approach leverages a data-efficient
procedure to learn a diffeomorphic transformation that maps simple stable
dynamical systems onto complex robotic skills. By exploiting mathematical tools
from differential geometry, the method ensures that the learned skills fulfill
the geometric constraints imposed by the underlying manifolds, such as unit
quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices
for impedance, while preserving the convergence to a given target. The proposed
approach is firstly tested in simulation on a public benchmark, obtained by
projecting Cartesian data into UQ and SPD manifolds, and compared with existing
approaches. Apart from evaluating the approach on a public benchmark, several
experiments were performed on a real robot performing bottle stacking in
different conditions and a drilling task in cooperation with a human operator.
The evaluation shows promising results in terms of learning accuracy and task
adaptation capabilities.Comment: 16 pages, 10 figures, journa
Effective skill refinement: Focusing on process to ensure outcome
In contrast to the abundance of motor skill acquisition and performance research, there is a paucity of work which addresses how athletes with an already learnt and well-established skill may go about making a subtle change, or refinement, to that skill.
Accordingly, the purpose of this review paper is to provide a comprehensive overview of current understanding pertaining to such practice. Specifically, this review addresses deliberately initiated refinements to closed and self-paced skills (e.g., javelin throwing, golf swing and horizontal jumps). In doing so, focus is directed to three fundamental considerations within applied coaching practice and future research endeavours; the intended outcomes, process and evaluative measures of skill refinement. Conclusions suggest that skill refinement is not the same as skill acquisition or performing already learnt skills with high-levels of automaticity. Due to the complexity of challenge faced, refinements are best addressed as an interdisciplinary solution, with objective measures informing coach decision making
Merging Position and Orientation Motion Primitives
In this paper, we focus on generating complex robotic trajectories by merging
sequential motion primitives. A robotic trajectory is a time series of
positions and orientations ending at a desired target. Hence, we first discuss
the generation of converging pose trajectories via dynamical systems, providing
a rigorous stability analysis. Then, we present approaches to merge motion
primitives which represent both the position and the orientation part of the
motion. Developed approaches preserve the shape of each learned movement and
allow for continuous transitions among succeeding motion primitives. Presented
methodologies are theoretically described and experimentally evaluated, showing
that it is possible to generate a smooth pose trajectory out of multiple motion
primitives
Designing a Virtual Manikin Animation Framework Aimed at Virtual Prototyping
International audienceIn the industry, numerous commercial packages provide tools to introduce, and analyse human behaviour in the product's environment (for maintenance, ergonomics...), thanks to Virtual Humans. We will focus on control. Thanks to algorithms newly introduced in recent research papers, we think we can provide an implementation, which even widens, and simplifies the animation capacities of virtual manikins. In order to do so, we are going to express the industrial expectations as for Virtual Humans, without considering feasibility (not to bias the issue). The second part will show that no commercial application provides the tools that perfectly meet the needs. Thus we propose a new animation framework that better answers the problem. Our contribution is the integration - driven by need ~ of available new scientific techniques to animate Virtual Humans, in a new control scheme that better answers industrial expectations
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Stable dynamical systems are a flexible tool to plan robotic motions in
real-time. In the robotic literature, dynamical system motions are typically
planned without considering possible limitations in the robot's workspace. This
work presents a novel approach to learn workspace constraints from human
demonstrations and to generate motion trajectories for the robot that lie in
the constrained workspace. Training data are incrementally clustered into
different linear subspaces and used to fit a low dimensional representation of
each subspace. By considering the learned constraint subspaces as zeroing
barrier functions, we are able to design a control input that keeps the system
trajectory within the learned bounds. This control input is effectively
combined with the original system dynamics preserving eventual asymptotic
properties of the unconstrained system. Simulations and experiments on a real
robot show the effectiveness of the proposed approach
SciTech News Volume 70, No. 4 (2016)
Columns and Reports
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Science-Technology Division 4
SLA Annual Meeting 2016 Report (S. Kirk Cabeen Travel Stipend Award recipient) 6
Reflections on SLA Annual Meeting (Diane K. Foster International Student Travel Award recipient) 8
SLA Annual Meeting Report (Bonnie Hilditch International Librarian Award recipient)10
Chemistry Division 12
Engineering Division 15
Reflections from the 2016 SLA Conference (SPIE Digital Library Student Travel Stipend recipient)15
Fundamentals of Knowledge Management and Knowledge Services (IEEE Continuing Education Stipend recipient) 17
Makerspaces in Libraries: The Big Table, the Art Studio or Something Else? (by Jeremy Cusker) 19
Aerospace Section of the Engineering Division 21
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