Matthias Rolf

Goal Babbling Permits Direct Learning of Inverse Kinematics

We present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path-based sampling approach: we generate training data along paths, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced enables an efficient detection and resolution of inconsistent samples solely from directly observable data. We derive and illustrate the exploration and learning process with a low-dimensional kinematic example that provides direct insight into the bootstrapping process. We further show that the method scales for high dimensional problems, such as the Honda humanoid robot or hyperredundant planar arms with up to 50 degrees of freedom.

Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

We present an approach to learn the inverse kinematics of the “bionic handling assistant”-an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse kinematics on the fly. We show the success of the method in extensive real-world experiments on the nonstationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of nonstationary actuation ranges, drifting sensors, and morphological changes. The experiments provide the first substantial quantitative real-world evidence for the success of goal-directed bootstrapping schemes, moreover with the challenge of nonstationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.

Constant curvature continuum kinematics as fast approximate model for the Bionic Handling Assistant

We evaluate the use of continuum kinematics with constant curvature as a kinematic model for Festos “Bionic Handling Assistant” (BHA). We introduce a new, elegant, and parameterless method to deal with geometric singularities in stretched positions, which allows to capture pure elongations that are not naturally expressed by the toroidal deformations underlying the constant curvature assumption. The stability of the method is shown with numeric simulations. We evaluate how well this model describes the BHA by using real-world position measurements as quantitative ground truth and find a good match between model and real BHA, with only 1% relative error. The model provides a practical, and highly efficient tool for the simulation and experimentation with continuum robots and is available as free software library1.

Attention via Synchrony: Making Use of Multimodal Cues in Social Learning

Infants learning about their environment are confronted with many stimuli of different modalities. Therefore, a crucial problem is how to discover which stimuli are related, for instance, in learning words. In making these multimodal ldquobindings,rdquo infants depend on social interaction with a caregiver to guide their attention towards relevant stimuli. The caregiver might, for example, visually highlight an object by shaking it while vocalizing the objects name. These cues are known to help structuring the continuous stream of stimuli. To detect and exploit them, we propose a model of bottom-up attention by multimodal signal-level synchrony. We focus on the guidance of visual attention from audio-visual synchrony informed by recent adult-infant interaction studies. Consequently, we demonstrate that our model is receptive to parental cues during child-directed tutoring. The findings discussed in this paper are consistent with recent results from developmental psychology but for the first time are obtained employing an objective, computational model. The presence of ldquomultimodal mothereserdquo is verified directly on the audio-visual signal. Lastly, we hypothesize how our computational model facilitates tutoring interaction and discuss its application in interactive learning scenarios, enabling social robots to benefit from adult-like tutoring.

Efficient exploration and learning of whole body kinematics

We present a neural network approach to early motor learning. The goal is to explore the needs for boot-strapping the control of hand movements in a biologically plausible learning scenario. The model is applied to the control of hand postures of the humanoid robot ASIMO by means of full upper body movements. For training, we use an efficient online scheme for recurrent reservoir networks consisting of supervised backpropagation-decorrelation output adaptation and an unsupervised intrinsic plasticity reservoir optimization. We demonstrate that the network can acquire accurate inverse models for the highly redundant ASIMO, applying bi-manual target motions and exploiting all upper body degrees of freedom. We show that very few, but highly symmetric training data is sufficient to generate excellent generalization capabilities to untrained target motions. We also succeed in reproducing real motion recorded from a human demonstrator, massively differing from the training data in range and dynamics. The demonstrated generalization capabilities provide a fundamental prerequisite for an autonomous and incremental motor learning in an developmentally plausible way. Our exploration process - though not yet fully autonomous - clearly shows that goal-directed exploration can, in contrast to “babbling” of joints angles, be done very efficiently even for many degrees of freedom and non-linear kinematic configurations as ASIMOs.

Software abstractions for simulation and control of a continuum robot

The Bionic Handling Assistant is a new continuum robot which is manufactured in a rapid-prototyping procedure out of elastic polyamide. Its mechanical flexibility and low weight provide an enormous potential for physical human robot interaction. Yet, the elasticity and parallel continuum actuation design challenge standard approaches to deal with a robot from a control, simulation, and software modeling perspective. We investigate how the software abstractions of the existing Robot Control Interface (RCI) and the Compliant Control Architecture (CCA) can deal with this platform from a software modeling and software architectural perspective. We focus on three different challenges: the first challenge is to enable reasonable and hierarchical semantic abstractions of the robot. The second challenge is to develop hardware I/O abstractions for the prototypical and heterogeneous technical setup. The third challenge is to realize this in a flexible and reusable manner. We evaluate our approaches to the above challenges in a practical scenario in which the robot is controlled either in simulation or on the real robot.

RELIABLE INTEGRATION OF CONTINUOUS CONSTRAINTS INTO EXTREME LEARNING MACHINES

The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, monotonicity, or bounded curvature in the learned function to guarantee a reliable performance. We show that the extreme learning machine is particularly well suited for this task. Constraints involving arbitrary derivatives of the learned function are effectively implemented through quadratic optimization because the learned function is linear in its parameters, and derivatives can be derived analytically. We further provide a constructive approach to verify that discretely sampled constraints are generalized to continuous regions and show how local violations of the constraint can be rectified by iterative re-learning. We demonstrate the approach on a practical and challenging control problem from robotics, illustrating also how the proposed method enables learning from few data samples if additional prior knowledge about the problem is...

An active compliant control mode for interaction with a pneumatic soft robot

Bionic soft robots offer exciting perspectives for more flexible and safe physical interaction with the world and humans. Unfortunately, their hardware design often prevents analytical modeling, which in turn is a prerequisite to apply classical automatic control approaches. On the other hand, also modeling by means of learning is hardly feasible due to many degrees of freedom, high-dimensional state spaces and the softness properties like e.g. mechanical elasticity, which cause limited repeatability and complex dynamics. Nevertheless, the realization of basic control modes is important to leverage the potential of soft robots for applications. We therefore propose a hybrid approach combining classical and learning elements for the realization of an interactive control mode for an elastic bionic robot. It superimposes a low-gain feedback control with a feed-forward control based on a learned simplified model of the inverse dynamics which considers only equilibria of the robots dynamics. We demonstrate on the Bionic Handling Assistant how a respective inverse equilibrium model can be learned and effectively exploited for quick and agile control. In a second step, the control scheme is extended to an active compliant control mode. It implements a kind of gravitation compensation to allow for kinesthetic teaching of the robot based on the implicit knowledge of gravitational and mechanical forces that are encoded in the learned equilibrium model. We finally discuss that this control scheme may be implemented also on other soft robots to provide the avenue towards their applications in general manipulation tasks.

Explorative learning of inverse models: A theoretical perspective

We investigate the role of redundancy for exploratory learning of inverse functions, where an agent learns to achieve goals by performing actions and observing outcomes. We present an analysis of linear redundancy and investigate goal-directed exploration approaches, which are empirically successful, but hardly theorized except negative results for special cases, and prove convergence to the optimal solution. We show that the learning curves of such processes are intrinsically low-dimensional and S-shaped, which explains previous empirical findings, and finally compare our results to non-linear domains.

A multi-level control architecture for the bionic handling assistant

The bionic handling assistant is one of the largest soft continuum robots and very special in being a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other soft robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight integration of standard analytic tools, simulation, control, and state-of-the-art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for flexible experimentation toward compliant user-interaction, grasping, and online learning of internal models. Graphical Abstract