2,382 research outputs found
A quantitative taxonomy of human hand grasps
Background: A proper modeling of human grasping and of hand movements is fundamental for robotics,
prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific
literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively
justified.
Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on
biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40
healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for
several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic)
taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific
taxonomies provide similar results despite describing different parameters of hand movements, one being muscular
and the other kinematic.
Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical
structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the
qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements
can be divided into five movement categories defined based on the overall grasp shape, finger positioning and
muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic
hand grasping synergies.
Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what
has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
While holding and manipulating an object, humans track the object states
through vision and touch so as to achieve complex tasks. However, nowadays the
majority of robot research perceives object states just from visual signals,
hugely limiting the robotic manipulation abilities. This work presents a
tactile-enhanced generalizable 6D pose tracking design named TEG-Track to track
previously unseen in-hand objects. TEG-Track extracts tactile kinematic cues of
an in-hand object from consecutive tactile sensing signals. Such cues are
incorporated into a geometric-kinematic optimization scheme to enhance existing
generalizable visual trackers. To test our method in real scenarios and enable
future studies on generalizable visual-tactile tracking, we collect a real
visual-tactile in-hand object pose tracking dataset. Experiments show that
TEG-Track significantly improves state-of-the-art generalizable 6D pose
trackers in both synthetic and real cases
Physics-based motion planning for grasping and manipulation
This thesis develops a series of knowledge-oriented physics-based motion planning algorithms for grasping and manipulation in cluttered an uncertain environments. The main idea is to use high-level knowledge-based reasoning to define the manipulation constraints that define the way how robot should interact with the objects in the environment. These interactions are modeled by incorporating the physics-based model of rigid body dynamics in planning.
The first part of the thesis is focused on the techniques to integrate the knowledge with physics-based motion planning. The knowledge is represented in terms of ontologies, a prologbased knowledge inference process is introduced that defines the manipulation constraints.
These constraints are used in the state validation procedure of sampling-based kinodynamic motion planners. The state propagator of the motion planner is replaced by a physics-engine that takes care of the kinodynamic and physics-based constraints. To make the interaction humanlike, a low-level physics-based reasoning process is introduced that dynamically varies the control bounds by evaluating the physical properties of the objects. As a result, power efficient
motion plans are obtained. Furthermore, a framework has been presented to incorporate linear temporal logic within physics-based motion planning to handle complex temporal goals.
The second part of this thesis develops physics-based motion planning approaches to plan in cluttered and uncertain environments. The uncertainty is considered in 1) objects’ poses due to sensing and due to complex robot-object or object-object interactions; 2) uncertainty in the contact dynamics (such as friction coefficient); 3) uncertainty in robot controls. The solution is framed with sampling-based kinodynamic motion planners that solve the problem in open-loop,
i.e., it considers uncertainty while planning and computes the solution in such a way that it successfully moves the robot from the start to the goal configuration even if there is uncertainty in the system.
To implement the above stated approaches, a knowledge-oriented physics-based motion planning tool is presented. It is developed by extending The Kautham Project, a C++ based tool for sampling-based motion planning. Finally, the current research challenges and future research directions to extend the above stated approaches are discussedEsta tesis desarrolla una serie de algoritmos de planificación del movimientos para la aprehensión y la manipulación de objetos en entornos desordenados e inciertos, basados en la física y el conocimiento. La idea principal es utilizar el razonamiento de alto nivel basado en el conocimiento para definir las restricciones de manipulación que definen la forma en que el robot debería interactuar con los objetos en el entorno. Estas interacciones se modelan incorporando en la planificación el modelo dinámico de los sólidos rígidos. La primera parte de la tesis se centra en las técnicas para integrar el conocimiento con la planificación del movimientos basada en la física. Para ello, se representa el conocimiento mediante ontologías y se introduce un proceso de razonamiento basado en Prolog para definir las restricciones de manipulación. Estas restricciones se usan en los procedimientos de validación del estado de los algoritmos de planificación basados en muestreo, cuyo propagador de estado se susituye por un motor basado en la física que tiene en cuenta las restricciones físicas y kinodinámicas. Además se ha implementado un proceso de razonamiento de bajo nivel que permite adaptar los límites de los controles aplicados a las propiedades físicas de los objetos. Complementariamente, se introduce un marco de desarrollo para la inclusión de la lógica temporal lineal en la planificación de movimientos basada en la física. La segunda parte de esta tesis extiende el enfoque a planificación del movimiento basados en la física en entornos desordenados e inciertos. La incertidumbre se considera en 1) las poses de los objetos debido a la medición y a las interacciones complejas robot-objeto y objeto-objeto; 2) incertidumbre en la dinámica de los contactos (como el coeficiente de fricción); 3) incertidumbre en los controles del robot. La solución se enmarca en planificadores kinodinámicos basados en muestro que solucionan el problema en lazo abierto, es decir que consideran la incertidumbre en la planificación para calcular una solución robusta que permita mover al robot de la configuración inicial a la final a pesar de la incertidumbre. Para implementar los enfoques mencionados anteriormente, se presenta una herramienta de planificación del movimientos basada en la física y guiada por el conocimiento, desarrollada extendiendo The Kautham Project, una herramienta implementada en C++ para la planificación de movimientos basada en muestreo. Finalmente, de discute los retos actuales y las futuras lineas de investigación a seguir para extender los enfoques presentados
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect
Brain-computer interface (BCI) technology enables direct communication
between the brain and external devices, allowing individuals to control their
environment using brain signals. However, existing BCI approaches face three
critical challenges that hinder their practicality and effectiveness: a)
time-consuming preprocessing algorithms, b) inappropriate loss function
utilization, and c) less intuitive hyperparameter settings. To address these
limitations, we present \textit{NeuroKinect}, an innovative deep-learning model
for accurate reconstruction of hand kinematics using electroencephalography
(EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift
(GAL) tasks data with minimal preprocessing pipelines, subsequently improving
the computational efficiency. A notable improvement introduced by
\textit{NeuroKinect} is the utilization of a novel loss function, denoted as
. This loss function addresses the discrepancy
between correlation and mean square error in hand kinematics prediction.
Furthermore, our study emphasizes the scientific intuition behind parameter
selection to enhance accuracy. We analyze the spatial and temporal dynamics of
the motor movement task by employing event-related potential and brain source
localization (BSL) results. This approach provides valuable insights into the
optimal parameter selection, improving the overall performance and accuracy of
the \textit{NeuroKinect} model. Our model demonstrates strong correlations
between predicted and actual hand movements, with mean Pearson correlation
coefficients of 0.92 (0.015), 0.93 (0.019), and 0.83 (0.018) for
the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced
by low mean squared errors (MSE) of 0.016 (0.001), 0.015 (0.002), and
0.017 (0.005) for the X, Y, and Z dimensions, respectively
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.Comment: 30 pages, 19 figure
Image Understanding at the GRASP Laboratory
Research in the GRASP Laboratory has two main themes, parameterized multi-dimensional segmentation and robust decision making under uncertainty. The multi-dimensional approach interweaves segmentation with representation. The data is explained as a best fit in view of parametric primitives. These primitives are based on physical and geometric properties of objects and are limited in number. We use primitives at the volumetric level, the surface level, and the occluding contour level, and combine the results. The robust decision making allows us to combine data from multiple sensors. Sensor measurements have bounds based on the physical limitations of the sensors. We use this information without making a priori assumptions of distributions within the intervals or a priori assumptions of the probability of a given result
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