334 research outputs found
Analytic Model for Quadruped Locomotion Task-Space Planning
Despite the extensive presence of the legged locomotion in animals, it is
extremely challenging to be reproduced with robots. Legged locomotion is an
dynamic task which benefits from a planning that takes advantage of the
gravitational pull on the system. However, the computational cost of such
optimization rapidly increases with the complexity of kinematic structures,
rendering impossible real-time deployment in unstructured environments. This
paper proposes a simplified method that can generate desired centre of mass and
feet trajectory for quadrupeds. The model describes a quadruped as two bipeds
connected via their centres of mass, and it is based on the extension of an
algebraic bipedal model that uses the topology of the gravitational attractor
to describe bipedal locomotion strategies. The results show that the model
generates trajectories that agrees with previous studies. The model will be
deployed in the future as seed solution for whole-body trajectory optimization
in the attempt to reduce the computational cost and obtain real-time planning
of complex action in challenging environments.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin German
Reconstructing Null-space Policies Subject to Dynamic Task Constraints in Redundant Manipulators
We consider the problem of direct policy learning in situations where the policies are only
observable through their projections into the null-space of a set of dynamic, non-linear
task constraints. We tackle the issue of deriving consistent data for the learning of such
policies and make two contributions towards its solution. Firstly, we derive the conditions
required to exactly reconstruct null-space policies and suggest a learning strategy based on
this derivation. Secondly, we consider the case that the null-space policy is conservative
and show that such a policy can be learnt more easily and robustly by learning the
underlying potential function and using this as our representation of the policy
Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it's use for robust predictive control. However, in realistic domains, system dynamics often change based on unobserved external contexts such as work load or contact conditions with other objects. Previous multiple model approaches to solving this problem are restricted to finite, discrete contexts without any generalization and have been tested only on linear systems. We present a framework for estimation of context through hidden latent variable extraction -- solely from experienced (non-linear) dynamics. This work refines the multiple model formalism to bootstrap context separation from context-unlabeled data and enables simultaneous online context estimation, dynamics learning and control based on a consistent probabilistic formulation. Most importantly, it extends the framework to a continuous latent model representation of context under specific assumptions of load distribution
Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
We introduce a robust probabilistic approach
to modeling shape contours based on a low-
dimensional, nonlinear latent variable model.
In contrast to existing techniques that use
objective functions in data space without ex-
plicit noise models, we are able to extract
complex shape variation from noisy data.
Most approaches to learning shape models
slide observed data points around fixed con-
tours and hence, require a correctly labeled
‘reference shape’ to prevent degenerate so-
lutions. In our method, unobserved curves
are reparameterized to explain the fixed data
points, so this problem does not arise. The
proposed algorithms are suitable for use with
arbitrary basis functions and are applicable
to both open and closed shapes; their effec-
tiveness is demonstrated through illustrative
examples, quantitative assessment on bench-
mark data sets and a visualization task
2D Shape Classification and Retrieval
We present a novel correspondence-based technique for efficient shape classification and retrieval. Shape boundaries are described by a set of (ad hoc) equally spaced points avoiding the need to extract landmark points. By formulating the correspondence problem in terms of a simple generative mode
Robustness of VOR and OKR adaptation under kinematics and dynamics transformations
Many computational models of vestibulo-ocular
reflex (VOR) adaptation have been proposed, however none of
these models have explicitly highlighted the distinction between
adaptation to dynamics transformations, in which the intrinsic
properties of the oculomotor plant change, and kinematic transformations,
in which the extrinsic relationship between head
velocity and desired eye velocity changes (most VOR adaptation
experiments use kinematic transformations to manipulate the
desired response). We show that whether a transformation is
kinematic or dynamic in nature has a strong impact upon
the speed and stability of learning for different control architectures.
Specifically, models based on a purely feedforward
control architecture, as is commonly used in feedback-error
learning (FEL), are guaranteed to be stable under kinematic
transformations, but are susceptible to slow convergence and
instability under dynamics transformations. On the other hand,
models based on a recurrent cerebellar architecture [7] perform
well under dynamics but not kinematics transformations. We
apply this insight to derive a new model of the VOR/OKR
system which is stable against transformations of both the plant
dynamics and the task kinematics
Online Dynamic Motion Planning and Control for Wheeled Biped Robots
Wheeled-legged robots combine the efficiency of wheeled robots when driving
on suitably flat surfaces and versatility of legged robots when stepping over
or around obstacles. This paper introduces a planning and control framework to
realise dynamic locomotion for wheeled biped robots. We propose the Cart-Linear
Inverted Pendulum Model (Cart-LIPM) as a template model for the rolling motion
and the under-actuated LIPM for contact changes while walking. The generated
motion is then tracked by an inverse dynamic whole-body controller which
coordinates all joints, including the wheels. The framework has a hierarchical
structure and is implemented in a model predictive control (MPC) fashion. To
validate the proposed approach for hybrid motion generation, two scenarios
involving different types of obstacles are designed in simulation. To the best
of our knowledge, this is the first time that such online dynamic hybrid
locomotion has been demonstrated on wheeled biped robots
Learning Discontinuities with Product-of-Sigmoids for Switching between Local Models
Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth function
The role of feed-forward and feedback processes for closed-loop prosthesis control
<p>Abstract</p> <p>Background</p> <p>It is widely believed that both feed-forward and feed-back mechanisms are required for successful object manipulation. Open-loop upper-limb prosthesis wearers receive no tactile feedback, which may be the cause of their limited dexterity and compromised grip force control. In this paper we ask whether observed prosthesis control impairments are due to lack of feedback or due to inadequate feed-forward control.</p> <p>Methods</p> <p>Healthy subjects were fitted with a closed-loop robotic hand and instructed to grasp and lift objects of different weights as we recorded trajectories and force profiles. We conducted three experiments under different feed-forward and feed-back configurations to elucidate the role of tactile feedback (i) in ideal conditions, (ii) under sensory deprivation, and (iii) under feed-forward uncertainty.</p> <p>Results</p> <p>(i) We found that subjects formed economical grasps in ideal conditions. (ii) To our surprise, this ability was preserved even when visual and tactile feedback were removed. (iii) When we introduced uncertainty into the hand controller performance degraded significantly in the absence of either visual or tactile feedback. Greatest performance was achieved when both sources of feedback were present.</p> <p>Conclusions</p> <p>We have introduced a novel method to understand the cognitive processes underlying grasping and lifting. We have shown quantitatively that tactile feedback can significantly improve performance in the presence of feed-forward uncertainty. However, our results indicate that feed-forward and feed-back mechanisms serve complementary roles, suggesting that to improve on the state-of-the-art in prosthetic hands we must develop prostheses that empower users to correct for the inevitable uncertainty in their feed-forward control.</p
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