A neural integrator model for planning and value-based decision making of a robotics assistant

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.The work received financial support from FCT through the PhD fellowships PD/BD/128183/2016 and SFRH/BD/124912/2016, the project “Neurofield” (PTDC/MAT-APL/31393/2017) and the research centre CMAT within the project UID/MAT/00013/2013

Emotional design and human-robot interaction

Recent years have shown an increase in the importance of emotions applied to the Design field - Emotional Design. In this sense, the emotional design aims to elicit (e.g., pleasure) or prevent (e.g., displeasure) determined emotions, during human product interaction. That is, the emotional design regulates the emotional interaction between the individual and the product (e.g., robot). Robot design has been a growing area whereby robots are interacting directly with humans in which emotions are essential in the interaction. Therefore, this paper aims, through a non-systematic literature review, to explore the application of emotional design, particularly on Human-Robot Interaction. Robot design features (e.g., appearance, expressing emotions and spatial distance) that affect emotional design are introduced. The chapter ends with a discussion and a conclusion.info:eu-repo/semantics/acceptedVersio

Online Monitoring of Object Detection Performance During Deployment

During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector’s performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.Quazi Marufur Rahman, Niko Sünderhauf and Feras Dayou

Bringing robotics closer to students - a threefold approach

In this paper, we present our threefold concept of "bringing robotics closer to students". Our efforts begin with motivating high-school students to study engineering sciences by increasing their interest in technical issues. This is achieved by a national robotics contest. Besides our work with high-school students, we also focus on projects for undergraduate university students. Several robotics projects and competitions are included into the curriculum of electrical and mechanical engineering and computer science. These projects offer chances to do special assignments and student research projects. It may be important to point out that most of the work described in this paper, including inventing and specifying the projects and organizing the events, has been done by undergraduate and graduate student

RoboKing - Bringing robotics closer to pupils

In this paper, we introduce RoboKing, a national contest of mobile autonomous robots, dedicated to teams of high school students. RoboKing differs from similar contests by supporting the participating teams with a 250 Euro voucher and by not restricting the kind of materials the robots can be build with. Its task is manageable for students without previous knowledge in robotics but offers enough complexity to be challenging for advanced participants. The first RoboKing contest with 12 participating teams from different parts of Germany took place at the Hannover Messe in 2004. Because of its great success, RoboKing will be held annually. RoboKing 2005 has been extended to 20 teams of pupils and offers a new challenging task. More pictures and video files can be found under www. roboking. de

Visual Odometry using sparse bundle adjustment on an autonomous outdoor vehicle

Visual Odometry is the process of estimating the movement of a (stereo) camera through its environment by matching point features between pairs of consecutive image frames. No prior knowledge of the scene nor the motion is necessary. In this work, we present a visual odometry approach using a specialized method of sparse bundle adjustment. We show experimental results that proof our approach to be a feasible method for estimating motion in unstructured outdoor environments

Quantum chaos in the Brownian SYK model with large finite N : OTOCs and tripartite information

We consider the Brownian SYK model of $N$ interacting Majorana fermions, with random couplings that are taken to vary independently at each time. We study the out-of-time-ordered correlators (OTOCs) of arbitrary observables and the R\'enyi-$2$ tripartite information of the unitary evolution operator, which were proposed as diagnostic tools for quantum chaos and scrambling, respectively. We show that their averaged dynamics can be studied as a quench problem at imaginary times in a model of $N$ qudits, where the Hamiltonian displays site-permutational symmetry. By exploiting a description in terms of bosonic collective modes, we show that for the quantities of interest the dynamics takes place in a subspace of the effective Hilbert space whose dimension grows either linearly or quadratically with $N$, allowing us to perform numerically exact calculations up to $N = 10^6$. We analyze in detail the interesting features of the OTOCs, including their dependence on the chosen observables, and of the tripartite information. We observe explicitly the emergence of a scrambling time $t^\ast\sim \ln N$ controlling the onset of both chaotic and scrambling behavior, after which we characterize the exponential decay of the quantities of interest to the corresponding Haar scrambled values.Comment: 25 pages, 13 figure

Meaningful maps with object-oriented semantic mapping

For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation.Niko Sünderhauf, Trung T. Pham, Yasir Latif, Michael Milford, Ian Rei

Localization with random time-periodic quantum circuits

We consider a random time evolution operator composed of a circuit of random unitaries coupling even and odd neighboring spins on a chain in turn. In spirit of Floquet evolution, the circuit is time-periodic; each timestep is repeated with the same random instances. We obtain analytical results for arbitrary local Hilbert space dimension d: On a single site, average time evolution acts as a depolarising channel. In the spin 1/2 (d=2) case, this is further quantified numerically. For that, we develop a new numerical method that reduces complexity by an exponential factor. Haar-distributed unitaries lead to full depolarization after many timesteps, i.e. local thermalization. A unitary probability distribution with tunable coupling strength allows us to observe a many-body localization transition. In addition to a spin chain under a unitary circuit, we consider the analogous problem with Gaussian circuits. We can make stronger statements about the entire covariance matrix instead of single sites only, and find that the dynamics is localising. For a random time evolution operator homogeneous in space, however, the system delocalizes

Quantum Computation for Periodic Solids in Second Quantization

In this work, we present a quantum algorithm for ground-state energy calculations of periodic solids on error-corrected quantum computers. The algorithm is based on the sparse qubitization approach in second quantization and developed for Bloch and Wannier basis sets. We show that Wannier functions require less computational resources with respect to Bloch functions because: (i) the L$_1$ norm of the Hamiltonian is considerably lower and (ii) the translational symmetry of Wannier functions can be exploited in order to reduce the amount of classical data that must be loaded into the quantum computer. The resource requirements of the quantum algorithm are estimated for periodic solids such as NiO and PdO. These transition metal oxides are industrially relevant for their catalytic properties. We find that ground-state energy estimation of Hamiltonians approximated using 200--900 spin orbitals requires {\it ca.}~$10{}^{10}$--$10^{12}$ T gates and up to $3\cdot10^8$ physical qubits for a physical error rate of $0.1\%$.Comment: Published in Physical Review Research 23 March 202