Real-Time Shape Estimation for Concentric Tube Continuum Robots with a Single Force/Torque Sensor.

Shape-sensing in real-time is a key requirement for the development of advanced algorithms for concentric tube continuum robots when safe interaction with the environment is important e.g., for path planning, advanced control, and human-machine interaction.

Let's Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human-Robot Interaction at Work.

Objective: This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human-robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness).

Background: Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital.

Goal-related feedback guides motor exploration and redundancy resolution in human motor skill acquisition.

The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood, such as the mastery of tools, musical instruments or sports. How novel sensorimotor skills are learned from scratch is yet largely unknown. In particular, the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements (motor redundancy).

Bootstrapping of Parameterized Skills Through Hybrid Optimization in Task and Policy Spaces.

Modern robotic applications create high demands on adaptation of actions with respect to variance in a given task. Reinforcement learning is able to optimize for these changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. We propose a parameterized skill that generalizes to new actions for changing task parameters, which is encoded as a meta-learner that provides parameters for task-specific dynamic motion primitives.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics.

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

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.

Rare neural correlations implement robotic conditioning with delayed rewards and disturbances.

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards.

Solving the distal reward problem with rare correlations.

In the course of trial-and-error learning, the results of actions, manifested as rewards or punishments, occur often seconds after the actions that caused them. How can a reward be associated with an earlier action when the neural activity that caused that action is no longer present in the network? This problem is referred to as the distal reward problem. A recent computational study proposes a solution using modulated plasticity with spiking neurons and argues that precise firing patterns in the millisecond range are essential for such a solution.

Online learning and generalization of parts-based image representations by non-negative sparse autoencoders.

We present an efficient online learning scheme for non-negative sparse coding in autoencoder neural networks. It comprises a novel synaptic decay rule that ensures non-negative weights in combination with an intrinsic self-adaptation rule that optimizes sparseness of the non-negative encoding. We show that non-negativity constrains the space of solutions such that overfitting is prevented and very similar encodings are found irrespective of the network initialization and size.

Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning.

We propose to use a biologically motivated learning rule based on neural intrinsic plasticity to optimize reservoirs of analog neurons. This rule is based on an information maximization principle, it is local in time and space and thus computationally efficient. We show experimentally that it can drive the neurons' output activities to approximate exponential distributions.

Learning lateral interactions for feature binding and sensory segmentation from prototypic basis interactions.

We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and segmentation problems. A predefined target segmentation can be achieved as attractor states of this linear threshold recurrent network, if the lateral weights are chosen by Hebbian learning.

The dynamic wave expansion neural network model for robot motion planning in time-varying environments.

We introduce a new type of neural network--the dynamic wave expansion neural network (DWENN)--for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model.