Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction

In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experiences (stored in the episodic and procedural memories) to shrink the search space by using reduced parameters bounds computed from the best optimizations realized by the robot with similar tasks of the new one (e.g. bin-picking from an homogenous heap of a similar object, based on visual similarity of objects stored in the semantic memory). As example, we have confronted the system to the constrained optimizations of 9 continuous hyperparameters for a professional software (Kamido) in industrial robotic arm bin-picking tasks, a step that is needed each time to handle correctly new object. We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations for each optimization) for every object tested (p-value=0.036).Comment: Accepted at the IEEE International Conference on Development and Learning and Epigenetic Robotics 2020 (ICDL-Epirob 2020

Online Language Learning to Perform and Describe Actions for Human-Robot Interaction

International audienceThe goal of this research is to provide a real-time and adaptive spoken langue interface between humans and a humanoid robot. The system should be able to learn new grammatical constructions in real-time, and then use them immediately following or in a later interactive session. In order to achieve this we use a recurrent neural network of 500 neurons-echo state network with leaky neurons [1]. The model processes sentences as grammatical constructions, in which the semantic words (nouns and verbs) are extracted and stored in working memory, and the grammatical words (prepositions, auxiliary verbs, etc.) are inputs to the network. The trained network outputs code the role (predicate, agent, object/location) that each semantic word takes. In the final output, the stored semantic words are then mapped onto their respective roles. The model thus learns the mappings between the grammatical structure of sentences and their meanings. The humanoid robot is an iCub [2] who interacts around a instrumented tactile table (ReacTable TM) on which objects can be manipulated by both human and robot. A sensory system has been developed to extract spatial relations. A speech recognition and text to speech off-the-shelf tool allows spoken communication. In parallel, the robot has a small set of actions (put(object, location), grasp(object), point(object)). These spatial relations, and action definitions form the meanings that are to be linked to sentences in the learned grammatical constructions. The target behavior of the system is to learn two conditions. In action performing (AP), the system should learn to generate the proper robot command, given a spoken input sentence. In scene description (SD), the system should learn to describe scenes given the extracted spatial relation. Training corpus for the neural model can be generated by the interaction with the user teaching the robot by describing spatial relations or actions, creating pairs. It could also be edited by hand to avoid speech recognition errors. These interactions between the different components of the system are shown in the Figure 1. The neural model processes grammatical constructions where semantic words (e.g. put, grasp, toy, left, right) are replaced by a common marker. This is done with only a predefined set of grammatical words (after, and, before, it, on, the, then, to, you). Therefore the model is able to deal with sentences that have the same constructions than previously seen sentences. In the AP condition, we demonstrate that the model can learn and generalize to complex sentences including "Before you put the toy on the left point the drums."; the robot will first point the drums and then put the toy on the left: showing here that the network is able to establish the proper chronological order of actions. Likewise, in the SD condition, the system can be exposed to a new scene and produce a description such as "To the left of the drums and to the right of the toy is the trumpet." In future research we can exploit this learning system in the context of human language development. In addition, the neural model could enable errors recovery from speech to text recognition. Index Terms: human-robot interaction, echo state network, online learning, iCub, language learning. References [1] H. Jaeger, "The "echo state" approach to analysing and training recurrent neural networks", Tech. Rep. GMD model has been developed with Oger toolbox: http://reservoir-computing.org/organic/engine. Figure 1: Communication between the speech recognition tool (that also controls the robotic platform) and the neural model

Les effets du TSCG sur la Région de Bruxelles-Capitale et la COCOM

La présente évaluation porte sur les effets du Traité sur la stabilité, la coordination et la gouvernance dans l’Union économique et monétaire (TSCG) au sein de la Région de Bruxelles-Capitale et de la COCOM. Cette évaluation est prévue dans les ordonnances1 qui ratifient le TSCG et qui transposent les règles budgétaires visées à l’article 3, § 1er du TSCG, adoptée par l’Assemblée réunie de la COCOM et le Parlement bruxellois. L’Institut Bruxellois de Statistique et d’Analyse (IBSA) est chargé d’exécuter cette évaluation pour ces deux instances. Les conséquences générées par la mise en œuvre du volet budgétaire et de la coordination des politiques publiques prévues par le TSCG sont présentées dans ce rapport d’évaluation. L’analyse porte sur la période 2014‑2017, soit presque 4 ans. Cette évaluation est faite au travers des perceptions d’acteurs institutionnels issus d’instances régionales bruxelloises et de la COCOM. Les acteurs interrogés ont ainsi pu partager leurs connaissances sur les dispositions du TSCG et les effets qu’ils ont perçus de l’application de celui‑ci

Navigation Assistance for a BCI-controlled Humanoid Robot

International audienceWe present an assisted navigation scheme designed to control a humanoid robot via a brain computer interface in order to let it interact with the environment and with humans. The interface is based on the well-known steady-state visually evoked potentials (SSVEP) and the stimuli are integrated into the live feedback from the robot embedded camera displayed on a Head Mounted Display (HMD). One user controlled the HRP-2 humanoid robot in an experiment designed to measure the performance of the new navigation scheme based on visual SLAM feedback. The new navigation scheme performance is tested in an experience where the user is asked to navigate to a certain location in order to perform a task. It results that without the navigation assistance it is much more difficult to reach the appropriate pose for performing the task. The detailed results of the experiments are reported in this paper, and we discuss the possible improvements of our novel scheme

Desynchronization induced by time-varying network

The synchronous dynamics of an array of excitable oscillators, coupled via a generic graph, is studied. Non-homogeneous perturbations can grow and destroy synchrony, via a self-consistent instability which is solely instigated by the intrinsic network dynamics. By acting on the characteristic time-scale of the network modulation, one can make the examined system to behave as its (partially) averaged analogue. This result is formally obtained by proving an extended version of the averaging theorem, which allows for partial averages to be carried out. As a byproduct of the analysis, oscillation death is reported to follow the onset of the network-driven instability

A short G1 phase is an intrinsic determinant of naïve embryonic stem cell pluripotency

AbstractA short G1 phase is a characteristic feature of mouse embryonic stem cells (ESCs). To determine if there is a causal relationship between G1 phase restriction and pluripotency, we made use of the Fluorescence Ubiquitination Cell Cycle Indicator (FUCCI) reporter system to FACS-sort ESCs in the different cell cycle phases. Hence, the G1 phase cells appeared to be more susceptible to differentiation, particularly when ESCs self-renewed in the naïve state of pluripotency. Transitions from ground to naïve, then from naïve to primed states of pluripotency were associated with increased durations of the G1 phase, and cyclin E-mediated alteration of the G1/S transition altered the balance between self-renewal and differentiation. LIF withdrawal resulted in a lengthening of the G1 phase in naïve ESCs, which occurred prior to the appearance of early lineage-specific markers, and could be reversed upon LIF supplementation. We concluded that the short G1 phase observed in murine ESCs was a determinant of naïve pluripotency and was partially under the control of LIF signaling