60,375 research outputs found
Separation of Visual and Motor Workspaces During Targeted Reaching Results in Limited Generalization of Visuomotor Adaptation
Separating visual and proprioceptive information in terms of workspace locations during reaching movement has been shown to disturb transfer of visuomotor adaptation across the arms. Here, we investigated whether separating visual and motor workspaces would also disturb generalization of visuomotor adaptation across movement conditions within the same arm. Subjects were divided into four experimental groups (plus three control groups). The first two groups adapted to a visual rotation under a “dissociation” condition in which the targets for reaching movement were presented in midline while their arm performed reaching movement laterally. Following that, they were tested in an “association” condition in which the visual and motor workspaces were combined in midline or laterally. The other two groups first adapted to the rotation in one association condition (medial or lateral), then were tested in the other association condition. The latter groups demonstrated complete transfer from the training to the generalization session, whereas the former groups demonstrated substantially limited transfer. These findings suggest that when visual and motor workspaces are separated, two internal models (vision-based one, proprioception-based one) are formed, and that a conflict between the two disrupts the development of an overall representation that underlies adaptation to a novel visuomotor transform
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems
In this letter, we propose a model parameter identification method via a
hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient
explore-exploit strategy to identify the parameters of dynamic models in a
data-driven optimization manner. We utilize our method for model parameter
identification of the AV-21, a full-scaled autonomous race vehicle. We then
incorporate the optimized parameters for the design of model-based planning and
control systems of our platform. In experiments, MI-HPO exhibits more than 13
times faster convergence than traditional parameter identification methods.
Furthermore, the parametric models learned via MI-HPO demonstrate good fitness
to the given datasets and show generalization ability in unseen dynamic
scenarios. We further conduct extensive field tests to validate our model-based
system, demonstrating stable obstacle avoidance and high-speed driving up to
217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The
source code for our work and videos of the tests are available at
https://github.com/hynkis/MI-HPO.Comment: 6 pages, 8 figures. Published in IEEE Control Systems Letters (L-CSS
A Framework of Hybrid Force/Motion Skills Learning for Robots
Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table
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
Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment
It is generally thought that skilled behavior in human beings results from a
functional hierarchy of the motor control system, within which reusable motor
primitives are flexibly integrated into various sensori-motor sequence patterns.
The underlying neural mechanisms governing the way in which continuous
sensori-motor flows are segmented into primitives and the way in which series of
primitives are integrated into various behavior sequences have, however, not yet
been clarified. In earlier studies, this functional hierarchy has been realized
through the use of explicit hierarchical structure, with local modules
representing motor primitives in the lower level and a higher module
representing sequences of primitives switched via additional mechanisms such as
gate-selecting. When sequences contain similarities and overlap, however, a
conflict arises in such earlier models between generalization and segmentation,
induced by this separated modular structure. To address this issue, we propose a
different type of neural network model. The current model neither makes use of
separate local modules to represent primitives nor introduces explicit
hierarchical structure. Rather than forcing architectural hierarchy onto the
system, functional hierarchy emerges through a form of self-organization that is
based on two distinct types of neurons, each with different time properties
(“multiple timescales”). Through the introduction of
multiple timescales, continuous sequences of behavior are segmented into
reusable primitives, and the primitives, in turn, are flexibly integrated into
novel sequences. In experiments, the proposed network model, coordinating the
physical body of a humanoid robot through high-dimensional sensori-motor
control, also successfully situated itself within a physical environment. Our
results suggest that it is not only the spatial connections between neurons but
also the timescales of neural activity that act as important mechanisms leading
to functional hierarchy in neural systems
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
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