Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models

Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning

Vollmer A-L, Mühlig M, Steil JJ, et al. Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning. PLoS ONE. 2014;9(3): e91349.Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction

Accumulation of α-synuclein mediates podocyte injury in Fabry nephropathy

Current therapies for Fabry disease are based on reversing intracellular accumulation of globotriaosylceramide (Gb3) by enzyme replacement therapy (ERT) or chaperone-mediated stabilization of the defective enzyme, thereby alleviating lysosomal dysfunction. However, their effect in the reversal of end-organ damage, like kidney injury and chronic kidney disease, remains unclear. In this study, ultrastructural analysis of serial human kidney biopsies showed that long-term use of ERT reduced Gb3 accumulation in podocytes but did not reverse podocyte injury. Then, a CRISPR/Cas9–mediated α-galactosidase knockout podocyte cell line confirmed ERT-mediated reversal of Gb3 accumulation without resolution of lysosomal dysfunction. Transcriptome-based connectivity mapping and SILAC-based quantitative proteomics identified α-synuclein (SNCA) accumulation as a key event mediating podocyte injury. Genetic and pharmacological inhibition of SNCA improved lysosomal structure and function in Fabry podocytes, exceeding the benefits of ERT. Together, this work reconceptualizes Fabry-associated cell injury beyond Gb3 accumulation, and introduces SNCA modulation as a potential intervention, especially for patients with Fabry nephropathy.publishedVersio

A Whole Systems Approach to Robot Imitation Learning of Object Movement Skills

Mühlig M. A Whole Systems Approach to Robot Imitation Learning of Object Movement Skills. Bielefeld University; 2011.Imitation learning has become a popular paradigm to extend the abilities of robots by demonstrating new skills. Many methods have been proposed that allow a robot to detect such demonstrations and to learn from them. However, there is a significant drawback with state-of-the-art imitation learning approaches. Most of them consider imitation learning of object movement skills as an isolated, datadriven method for learning object trajectories. They neglect much of the information that is provided by the human interaction partner and therefore miss many opportunities for increasing the generalization capabilities. The work at hand addresses this drawback by proposing a whole systems view on imitation learning. The main contribution of this thesis is an architecture for interactive imitation learning of object movement skills. Its purpose is to enable learning skills from only a few demonstrations, but still to be able to extensively generalize to new situations. This is achieved by various methods. Firstly, a probabilistic learning scheme in combination with movement optimization is suggested. It allows to exploit the variance information from multiple demonstrations. While imitating, the robot can diverge from variant parts of the movement to respect additional criteria regarding the robot's limits. Furthermore, a novel method for automatically selecting skill-dependent task spaces is presented. These task spaces represent a skill in relative coordinates of specific object feature points, such as their top or bottom sides. That way, the learned skill is decoupled from specific objects and from the robot's embodiment. In particular, it enables the robot to perform a skill in different ways, such as one-handed or bimanually. This is achieved by introducing the concept of a dynamic body schema. All of the presented methods respect that learning is performed in interaction with a human tutor. The tutor is modelled by the system, which allows to detect certain postures for instructing the robot. Additionally, the model is used to estimate the non-measurable internal state of the tutor, like the effort or discomfort of certain poses. This allows to deduce skill-relevance of specific phases of a demonstrated movement. The presented system also comprises an attention mechanism, which is directly coupled to the robot control scheme using the novel concept of linked objects. Consequently, the tutor can highlight relevant objects, from which the robot either learns or to which it applies a learned skill. All of the proposed methods are not presented in isolation. Instead, the thesis emphasizes the whole systems view by integrating them into a consistent architecture. The generalization capabilities of this architecture go beyond the state of the art, which is validated by several experiments. For instance, one experiment shows that a child-sized humanoid robot with 26 degrees of freedom is able to learn the skill of stacking objects. In addition to imitating the skill as demonstrated, the robot is able to generalize it to different objects, situations that contain obstacles, and to a bimanual performance. Even more, the skill learned by the humanoid robot can also be reproduced by other robots

Demonstrating actions to a robot: How na\ive users adapt to a robot’s replication of goal and manner-oriented actions

Vollmer A-L, Mühlig M, Rohlfing K, Wrede B, Cangelosi A. Demonstrating actions to a robot: How na\ive users adapt to a robot’s replication of goal and manner-oriented actions. In: Fernández R, Isard A, eds. SEMDIAL 2013 DialDam. Proceedings of the 17th Workshop on the Semantics and Pragmatics of Dialogue : Amsterdam, 16-18 December 2013. Proceedings SemDial. Amsterdam: Univ. of Amsterdam; 2013: 240-242

Action learning concept graphics.

<p>(A) Unidirectional concept of current imitation learning approaches: The tutor demonstrates the action (white oval) according to his/her knowledge (upper hatched oval). The learner passively observes the action demonstration and learns the action. (B) Interactionist concept of learning: The tutor demonstrates the action (upper white oval) corresponding to his/her knowledge (upper hatched oval) emphasizing what is relevant to the action accordingly. The learner's level of understanding or knowledge of the action (lower hatched oval) is communicated by his/her feedback (lower white oval). This feedback directly influences the tutor's action demonstration. The tutor monitors the learner's feedback, builds hypotheses about the learner's understanding, and reacts by changing his/her demonstration accordingly as will be shown in this contribution.</p

Items, task instructions, and example trajectories.

<p>Items, task instructions, and example trajectories.</p

Statistical results of turn-based feedback influence on number of action demonstrations (repetitions).

<p>Statistical results of turn-based feedback influence on number of action demonstrations (repetitions).</p