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.

Multirobot systems must be able to maintain performance when robots get delayed during execution. For mobile robots, one source of delays iscongestion. Congestion occurs when robots deployed in shared physical spaces interact, as robots present in the same area simultaneously must maneuver to avoid each other. Congestion can adversely affect navigation performance and increase the duration of navigation actions. In this article, we present a multirobot planning framework that utilizes learnt probabilistic models of how congestion affects navigation duration. Central to our framework is aprobabilistic reservation table, which summarizes robot plans, capturing the effects of congestion. To plan, we solve a sequence of single-robottime-varying Markov automata, where transition probabilities and rates are obtained from the probabilistic reservation table. We also present an iterative model refinement procedure for accurately predicting execution-time robot performance. We evaluate our framework with extensive experiments on synthetic data and simulated robot behavior.

This paper presents an effective method for planning and optimizing robot motions in joint space using via-points. The via-point formulation allows for a sparse movement representation which is inherently smooth according to a minimum jerk criterion. In this research we focus on two aspects. First, we present an initialization method to find a feasible number of via-points and their distribution in time. This initialization takes the difficulty of the planning problem into account and finds a tradeoff between representation complexity (number of via-points) and optimization time. Second, we propose a two-step optimization approach to improve the via-point parameters to satisfy constraints over the whole motion duration. We show that our methods generate feasible and smooth robot motions with a high success rate for tasks of varying complexity. The methods are evaluated in simulation using four different scenarios with two different robots, a 7-DOF robot arm and a 16-DOF humanoid upper body. Additionally, the experiments with the robot arm are shown in a real-world experiment.

This paper introduces a motion generation framework that integrates a hierarchical movement primitive (MP) layer with optimal control in form of receding horizon optimization. In order to benefit from fast reactions on the MP-layer, the optimal control layer can be overridden in risky situations to generate quick, though non-optimal solutions. By this, the system fulfills four desirable properties. It continuously adapts the robot's motion without noticeable delay (1) by optimizing for collision and joint limit avoidance based on a future time horizon instead of the current state only (2). It accounts for the full robot motion that may result from multiple active MPs at the same time (3) and despite a possibly slow optimization still provides the robustness and quick reaction capabilities of MPs (4). The framework has been validated in an experiment in which a humanoid robot performed a task, optimized wrt. collisions and joint limit avoidance, but still could react within 50 ms after detection of a potential risk.

This paper addresses the question of how to create adaptive and smooth sequences of actions and how to decide among skill options in a continuous manner without the necessity of recurrent planning. Motion generation is based on serial and parallel blending of movement primitives (MP). MPs are modeled as dynamical systems on task coordinates with attractor behavior and augmented with additional signals to ease their coordination. Sequences and transitions between skills are realized in a unified way as bifurcating dynamical systems based on continuous-time recurrent neural networks. The neural output is used as activation signal for MPs. Besides continuous feedback from the controlled MPs, the neural dynamics is influenced by a cost term from a future prediction to allow the inhibition of an action flow that is expected to fail. First results are shown in a physical simulation environment on a high-DoF robotic hand-arm system. The system is capable of creating smooth transients of MPs. Robustness to disturbances can be observed as local adaptations of individual low-level MPs, flexible sequencing of MPs, and global error recovery by changing the whole strategy of how to perform a movement skill.

Humanoid robots are intended to act and interact in dynamically changing environments in the presence of humans. Current robotic systems are usually able to move in dynamically changing environments because of an inbuilt depth and obstacle sensing. However, for acting in their environment the internal representation of such systems is usually constructed by hand and known in advance. In contrast, this paper presents a system that dynamically constructs its internal scene representation using a model-based vision approach. This enables our system to approach and grasp objects in an previously unknown scene. We combine standard stereo with model-based image fitting techniques for a real-time estimation of the position and orientation of objects. The model-based image processing allows for an easy transfer to the internal, dynamic scene representation. For movement generation we use a task-level whole-body control approach that is coupled with a movement optimization scheme. Furthermore, we present a novel method that constrains the robot to keep certain objects in the FOV while moving. We demonstrate the successful interplay between model-based vision, dynamic scene representation, and movement generation by means of some interactive reaching and grasping tasks.

This paper presents an architecture for learning and reproducing movements with a robot in interaction with a human teacher. We focus on the movement representation and propose three enhancements to increase generalization capabilities: Firstly, we introduce a flexible task-level movement representation that is based on neuropsychological findings. Movement is represented in task-oriented frames of reference, and generalizes to a variety of different situations. Secondly, we propose a mechanism to decouple the task descriptors from the perceived objects in the robot's environment. This allows to formulate a set of generic controllers, and to interactively create associations with perceived objects. Thirdly, we introduce a method to dynamically modify the system's body schema to account for structural changes such as having grasped a tool. The changes are consistently treated in the kinematics computations. This permits to generalize movements to be carried out in different ways, for instance with different hands or bi-manually. A set of experiments in an interactive imitation learning situation underline the capabilities of the proposed concepts.

In this paper we present a new robot control and learning framework. By integrating previously presented as well as new methods, the robot is able to learn an invariant and generic movement representation from a human tutor. We argue that in order to apply such generic representations to new situations and thus create a flexible system, the use of interaction is beneficial. The interaction is based on a kinematically controlled model of a human tutor, which is used as a model-based filter and also for recognizing postures that influence the interaction. In addition, a new movement segmentation scheme is presented that is based on correlating movements by the tutor's hand with the salient objects in the scene. The focus of this paper is on the interactive learning aspects of the system and particular emphasis is given to an experiment in which the humanoid robot ASIMO learns from a human tutor. The system includes extensive generalization capabilities that result from an online adaption of the robot's body schema and the exploitation of inter-trial variance from multiple demonstrations. This enables the robot to reproduce the movement in new situations. For example, a stacking task that the tutor performed one-handed can be executed bi-manually by the robot.

Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.

Previous work [1] shows that the movement representation in task spaces offers many advantages for learning object-related and goal-directed movement tasks through imitation. It allows to reduce the dimensionality of the data that is learned and simplifies the correspondence problem that results from different kinematic structures of teacher and robot. Further, the task space representation provides a first generalization, for example wrt. differing absolute positions, if bi-manual movements are represented in relation to each other. Although task spaces are widely used, even if they are not mentioned explicitly, they are mostly defined a priori. This work is a step towards an automatic selection of task spaces. Observed movements are mapped into a pool of possibly even conflicting task spaces and we present methods that analyze this task space pool in order to acquire task space descriptors that match the observation best. As statistical measures cannot explain importance for all kinds of movements, the presented selection scheme incorporates additional criteria such as an attention-based measure. Further, we introduce methods that make a significant step from purely statistically-driven task space selection towards model-based movement analysis using a simulation of a complex human model. Effort and discomfort of the human teacher is being analyzed and used as a hint for important task elements. All methods are validated with real-world data, gathered using color tracking with a stereo vision system and a VICON motion capturing system.