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We learn models to generate the immediate future in video. This problem has two main challenges. Firstly, since the future is uncertain, models should be multi-modal, which can be difficult to learn. Secondly, since the future is similar to the past, models store low-level details, which complicates learning of high-level semantics. We propose a framework to tackle both of these challenges. We present...
We present a framework from vision based hand movement prediction in a real-world human-robot collaborative scenario for safety guarantee. We first propose a perception submodule that takes in visual data solely and predicts human collaborator's hand movement. Then a robot trajectory adaptive planning submodule is developed that takes the noisy movement prediction signal into consideration for optimization...
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a...
In this paper, we propose a method for improving the maneuverability of master-slave systems. We aim at reproducing human skillfulness and dynamic performance in master-slave robots by using assist control for human operators. In this paper, we tackle a reaching task performed by a master-slave robot and propose an operation assist algorithm based on visual feedback control. The algorithm consists...
We address the problem of modeling complex target behavior using a stochastic model that integrates object dynamics, statistics gathered from the environment and semantic knowledge about the scene. The method exploits prior knowledge to build point-wise polar histograms that provide the ability to forecast target motion to the most likely paths. Physical constraints are included in the model through...
Knowledge of the physical properties of objects is essential in a wide range of robotic manipulation scenarios. A robot may not always be aware of such properties prior to interaction. If an object is incorrectly assumed to be rigid, it may exhibit unpredictable behavior when grasped. In this paper, we use vision based observation of the behavior of an object a robot is interacting with and use it...
The goal of our work is to acquire an internal model through a robot's experience. The internal model has the ability for mutual conversion between motor commands and movement of the body (e.g. hand) in view. Unlike other works, which assume the robot's body to be extracted in its view, we assume that external moving objects are also included in its view. We introduce predictability as a measure to...
Increasing performances of nonlinear predictive control techniques for visual servoing systems, by using image moments as visual features, is the main goal of this paper. An local model based predictor is developed and the cost function is constructed using a image moments based reference trajectory. A new type of visual predictive control architecture is designed and a simulator is developed. Simulation...
This paper describes a large-scale experimental study, in which a humanoid robot learned to press and detect doorbell buttons autonomously. The models for action selection and visual detection were grounded in the robot's sensorimotor experience and learned without human intervention. Experiments were performed with seven doorbell buttons, which provided auditory feedback when pressed. The robot learned...
The purpose of this work is to investigate the applicability of a visual tracking model on humanoid robots in order to achieve a human-like predictive behavior. In humans, in case of moving targets the oculomotor system uses a combination of the smooth pursuit eye movement and saccadic movements, namely “catch up” saccades to fixate the object of interest. This work aims to validate the "catch...
In teleoperation systems, operator performance is negatively affected by time-delayed visual feedback. Predictive display (PD) compensates for delays by providing synthesized visual feedback. While most existing PD methods rely on a priori models (e.g., from laser range finding or stereo vision), recent work on monocular SLAM and SFM makes it possible to acquire PD models in single camera applications...
In humans the tracking of a visual moving target across occlusions is not made with continuous smooth pursuit. The tracking stops when the object is occluded and one or two saccades are made to the other side of the occluder to anticipate when and where the object reappears. This paper describes a methodology for the implementation of such a behavior in a robotic platform - the iCub. We use the RLS...
In this paper, a model predictive control strategy is presented to visual servoing a robot manipulator with eye-in-hand configuration. Starting with the discrete form of the time derivative of features related with camera velocity through image Jacobian matrix and taking into account the discrete robot model, a new approach for computing the predictions is introduced. By means of a cost function based...
In this paper, we analyze the behavior of a simulated mobile robot, which interacts with an initially unknown maze-environment. The robot is controlled by an interactive system that is based on a model building Time Growing Neural Gas (TGNG) algorithm and a homeostatic motivational system, which activates movement preferences and goals within the emergent model structure for behavioral control. We...
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