Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets.

This paper proposes a novel method for jointly estimating the track of a moving object and the events in which it participates. The method is intended for dealing with generic objects that are hard to localise and track with the performance of current detection algorithms - our focus is on events involving carried objects. The tracks for other objects with which the target object interacts (e.g. the carrying person) are assumed to be given. The method is posed as maximisation of a posterior probability defined over event sequences and temporally-disjoint subsets of the tracklets from an earlier tracking process. The probability function is a Hidden Markov Model coupled with a term that penalises non-smooth tracks and large gaps in the observed data. We evaluate the method using tracklets output by three state of the art trackers on the new created MINDSEYE2015 dataset and demonstrate improved performance.

Saccades are fast eye movements that allow humans and robots to bring the visual target in the center of the visual field. Saccades are open loop with respect to the vision system, thus their execution require a precise knowledge of the internal model of the oculomotor system. In this work, we modeled the saccade control, taking inspiration from the recurrent loops between the cerebellum and the brainstem. In this model, the brainstem acts as a fixed-inverse model of the oculomotor system, while the cerebellum acts as an adaptive element that learns the internal model of the oculomotor system. The adaptive filter is implemented using a state-of-the-art neural network, called I-SSGPR. The proposed approach, namely recurrent architecture, was validated through experiments performed both in simulation and on an antropomorphic robotic head. Moreover, we compared the recurrent architecture with another model of the cerebellum, the feedback error learning. Achieved results show that the recurrent architecture outperforms the feedback error learning in terms of accuracy and insensitivity to the choice of the feedback controller.

This work aims to augment the capacities for haptic perception in the iCub robot to generate a controller for surface exploration. The main task involves moving the hand over an irregular surface with uncertain slope, by concurrently regulating the pressure of the contact. Providing this ability will enable the autonomous extraction of important haptic features, such as texture and shape. We propose a hand controller whose operational space is defined over the surface of contact. The surface is estimated using a robust probabilistic estimator, which is then used for path planning. The motor commands are generated using a feedback controller, taking advantage of the kinematic information available by proprioception. Finally, the effectiveness of this controller is extended using a cerebellar-like adapter that generates reliable pressure tracking over the finger and results in a trajectory with less vulnerability to perturbations. The results of this work are consistent with insights about the role of the cerebellum on haptic perception in humans.

The aim of this paper is to improve the skills of robotic systems in their interaction with nearby objects. The basic idea is to enhance visual estimation of objects in the world through the merging of different visual estimators of the same stimuli. A neuroscience-inspired model of stereoptic and perspective orientation estimators, merged according to different criteria, is implemented on a robotic setup and tested in different conditions. Experimental results suggest that the integration of multiple monocular and binocular cues can make robot sensory systems more reliable and versatile. The same results, compared with simulations and data from human studies, show that the model is able to reproduce some well-recognized neuropsychological effects.

This paper presents an approach to facial expression recognition based on the theory of liquid computing. Up to date, no emotion recognition systems based on spiking neural networks exist, and our work is the first attempt in this direction. We investigated the pattern recognition ability of Liquid State Machines based on various neural models, such as integrate-and-fire, resonate-and-fire, FitzHugh-Nagumo, Morris-Lecal, Hindmarsh-Rose and Izhikevich's models. No single Liquid State Machine provided particularly good results, but a global classifier we defined merging the response of the different models achieved a very satisfactory performance in expression recognition.

The properties of separation ability and computational efficiency of liquid state machines depend on the neural model employed and on the connection density in the liquid column. A simple model of part of mammalians visual system consisting of one hypercolumn was examined. Such a system was stimulated by two different input patterns, and the Euclidean distance, as well as the partial and global entropy of the liquid column responses were calculated. Interesting insights could be drawn regarding the properties of different neural models used in the liquid hypercolumn, and on the effect of connection density on the information representation capability of the system.

Integration of multiple visual cues provides natural systems with superior abilities in dealing with nearby objects. This research is aimed at verifying if robotic systems could also benefit from the merging of different visual cues of the same stimulus. A computational model of stereoscopic and perspective orientation estimators, merged according to different criteria, is implemented on a robotic setup and tested in different conditions. Experimental results suggest that the principle of cue integration can make robot sensory systems more reliable and robust. The same results compared with data from human studies show that the model is able to reproduce some well-known neuropsychological effects.

This paper presents and analyzes 12 quality measures that characterize robotic grips according to their stability and reliability. The measures are designed to assess three-finger grips of two-dimensional parts performed in a real environment, taking into account both theoretical aspects and unavoidable uncertainties of a grasping action. They build on the existing literature and on physical and mechanical considerations. The measures constitute a feature space that pattern recognition methods can use in order to classify robotic grips according to their quality. Six of the measures depend on the actual finger configuration of the gripper, and they have shown to be critical for better characterization. The kinematics of the Barrett Hand have been used. As a validation step, the measures are merged in two global quality values (with different practical applicability) that can be used to rank feasible candidate grips.

Learning techniques in robotic grasping applications have usually been concerned with the way a hand approaches to an object, or with improving the motor control of manipulation actions. We present an active learning approach devised to face the problem of visually-guided grasp selection. We want to choose the best hand configuration for grasping a particular object using only visual information. Experimental data from real grasping actions is used, and the experience gathering process is driven by an on-line estimation of the reliability assessment capabilities of the system. The goal is to improve the selection skills of the grasping system, minimizing at the same time the cost and duration of the learning process.

This paper presents and analyses ten criteria that assess the quality of a set of three-finger grips suitable for dextrous manipulation on real 2D parts. The set of candidate hand configurations is the result of a previous process of grasp generation from the object image. The proposed criteria include six that depend on the actual finger configuration of the gripper. The kinematics of the Barrett hand has been used. The criteria are merged to give a global quality value that can be used to select the best grip to execute. Experimental results include tests on stability and the effect of parameter variation.

This paper deals with visually guided grasping of unmodeled objects for robots which exhibit an adaptive behavior based on their previous experiences. Nine features are proposed to characterize three-finger grasps. They are computed from the object image and the kinematics of the hand. Real experiments on a humanoid robot with a Barrett hand are carried out to provide experimental data. This data is employed by a classification strategy, based on the k-nearest neighbour estimation rule, to predict the reliability of a grasp configuration in terms of five different performance classes. Prediction results suggest the methodology is adequate.