Conversational agents in the form of virtual agents or social robots are rapidly becoming wide-spread. Humans use non-verbal behaviors to signal their intent, emotions and attitudes in human-human interactions. Conversational agents therefore need this ability as well in order to make an interaction pleasant and efficient. An important part of non-verbal communication is gesticulation: gestures communicate a large share of non-verbal content. Previous systems for gesture production were typically rule-based and could not represent the range of human gestures. Recently the gesture generation field has shifted to data-driven approaches. We follow this line of research by extending the state-of-the-art deep-learning based model. Our model leverages representation learning to enhance speech-gesture mapping. We provide analysis of different representations for the input (speech) and the output (motion) of the network by both objective and subjective evaluations. We also analyze the importance of smoothing of the produced motion and emphasize how challenging it is to evaluate gesture quality. In the future we plan to enrich input signal by taking semantic context (text transcription) as well, make the model probabilistic and evaluate our system on the social robot NAO.
Fingertip skin friction plays a critical role during object manipulation. We will describe a simple and reliable method to estimate the fingertip static coefficient of friction (CF) continuously and quickly during object manipulation, and we will describe a global expression of the CF as a function of the normal force and fingertip moisture. Then we will show how skin hydration modifies the skin deformation dynamics during grip-like contacts. Certain motor behaviours observed during object manipulation could be explained by the effects of skin hydration. Then the biomechanics of the partial slip phenomenon will be described, and we will examine how this partial slip phenomenon is related to the subjective perception of fingertip slip.
Future cities and infrastructure systems will evolve into complex conglomerates where autonomous aerial, aquatic and ground-based robots will coexist with people and cooperate in symbiosis. To create this human-robot ecosystem, robots will need to respond more flexibly, robustly and efficiently than they do today. They will need to be designed with the ability to move across terrain boundaries and physically interact with infrastructure elements to perform sensing and intervention tasks. Taking inspiration from nature, aerial robotic systems can integrate multi-functional morphology, new materials, energy-efficient locomotion principles and advanced perception abilities that will allow them to successfully operate and cooperate in complex and dynamic environments. This talk will describe the scientific fundamentals, design principles and technologies for the development of biologically inspired flying robots with adaptive morphology that can perform monitoring and manufacturing tasks for future infrastructure and building systems. Examples will include flying robots with perching capabilities and origami-based landing systems, drones for aerial construction and repair, and combustion-based jet thrusters for aerial-aquatic vehicles.
Organizers: Metin Sitti
In this talk we will give an overview of research efforts within autonomous manipulation at the AASS Research Center, Örebro University, Sweden. We intend to give a holistic view on the historically separated subjects of robot motion planning and control. In particular, viewing motion behavior generation as an optimal control problem allows for a unified formulation that is uncluttered by a-priori domain assumptions and simplified solution strategies. Furthermore, We will also discuss the problems of workspace modeling and perception and how to integrate them in the overarching problem of autonomous manipulation.
Organizers: Ludovic Righetti
As large tensor-variate data increasingly become the norm in applied machine learning and statistics, complex analysis methods similarly increase in prevalence. Such a trend offers the opportunity to understand more intricate features of the data that, ostensibly, could not be studied with simpler datasets or simpler methodologies. While promising, these advances are also perilous: these novel analysis techniques do not always consider the possibility that their results are in fact an expected consequence of some simpler, already-known feature of simpler data (for example, treating the tensor like a matrix or a univariate quantity) or simpler statistic (for example, the mean and covariance of one of the tensor modes). I will present two works that address this growing problem, the first of which uses Kronecker algebra to derive a tensor-variate maximum entropy distribution that shares modal moments with the real data. This distribution of surrogate data forms the basis of a statistical hypothesis test, and I use this method to answer a question of epiphenomenal tensor structure in populations of neural recordings in the motor and prefrontal cortex. In the second part, I will discuss how to extend this maximum entropy formulation to arbitrary constraints using deep neural network architectures in the flavor of implicit generative modeling, and I will use this method in a texture synthesis application.
Organizers: Philipp Hennig
In classical reinforcement learning agents accept arbitrary short term loss for long term gain when exploring their environment. This is infeasible for safety critical applications such as robotics, where even a single unsafe action may cause system failure or harm the environment. In this work, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an a priori unknown safety constraint that depends on states and actions and satisfies certain regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm, SAFEMDP, for this task and prove that it completely explores the safely reachable part of the MDP without violating the safety constraint. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.
Organizers: Sebastian Trimpe
Organizers: Moritz Grosse-Wentrup
This is the story of the novel model predictive control (MPC) solution for ABB’s largest drive, the Megadrive LCI. LCI stands for load commutated inverter, a type of current source converter which powers large machineries in many industries such as marine, mining or oil & gas. Starting from a small software project at ABB Corporate Research, this novel control solution turned out to become the first time ever MPC was employed in a 48 MW commercial drive. Subsequently it was commissioned at Kollsnes, a key facility of the natural gas delivery chain, in order to increase the plant’s availability. In this presentation I will talk about the magic behind this success story, the so-called Embedded MPC algorithms, and my objective will be to demonstrate the possibilities when power meets computation.
Organizers: Sebastian Trimpe
Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns of a user into messages or commands for an interactive application. Such brain activity is typically measured using Electroencephalography (EEG), before being processed and classified by the system. EEG-based BCIs have proven promising for a wide range of applications ranging from communication and control for motor impaired users, to gaming targeted at the general public, real-time mental state monitoring and stroke rehabilitation, to name a few. Despite this promising potential, BCIs are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low robustness and reliability, as well as their long training times. In this talk I present some of our research aimed at addressing these points in order to make EEG-based BCIs usable, i.e., to increase their efficacy and efficiency. In particular, I will present a set of contributions towards this goal 1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and 2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful, e.g., for neuroergonomics or real-time brain activity and mental state visualization.
The predictive simulation of engineering systems increasingly rests on the synthesis of physical models and experimental data. In this context, Bayesian inference establishes a framework for quantifying the encountered uncertainties and fusing the available information. A summary and discussion of some recently emerged methods for uncertainty propagation (polynomial chaos expansions) and related MCMC-free techniques for posterior computation (spectral likelihood expansions, optimal transportation theory) is presented.
Organizers: Philipp Hennig
In this talk I am going to present the work we have been doing at the Computer Vision Lab of the Technical University of Munich which started as an attempt to better deal with videos (and therefore the time domain) within neural network architectures.
Organizers: Joel Janai
Kathleen is the creator of the well-known CAESAR anthropomorphic dataset and is an expert on body shape and apparel fit.
Organizers: Javier Romero
Underactuated mechanical systems (UMS) play an essential role in several branches of industrial activity and their application scope ranges from robotic manipulators and overhead cranes to aerospace vehicles and watercrafts. Despite this broad spectrum of applications, the problem of designing accurate controllers for underactuated systems is, however, much more tricky than for fully actuated ones. Moreover, the dynamic behavior of an UMS is frequently uncertain and highly nonlinear, which in fact makes the design of control schemes for such systems a challenge for conventional and well established methods. In this talk, it will be shown that intelligent algorithms, such as fuzzy logic and artificial neural networks, could be combined with nonlinear control techniques (feedback linearization or sliding modes) in order to improve both set-point regulation and trajectory tracking of uncertain underactuated mechanical systems.
Organizers: Sebastian Trimpe