Christian Emmerich

A user study on kinesthetic teaching of redundant robots in task and configuration space

The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robots movement generation. The explicit modeling of such criteria usually require experts to adapt the robots movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

Teaching nullspace constraints in physical human-robot interaction using Reservoir Computing

A major goal of current robotics research is to enable robots to become co-workers that collaborate with humans efficiently and adapt to changing environments or workflows. We present an approach utilizing the physical interaction capabilities of compliant robots with data-driven and model-free learning in a coherent system in order to make fast reconfiguration of redundant robots feasible. Users with no particular robotics knowledge can perform this task in physical interaction with the compliant robot, for example to reconfigure a work cell due to changes in the environment. For fast and efficient learning of the respective null-space constraints, a reservoir neural network is employed. It is embedded in the motion controller of the system, hence allowing for execution of arbitrary motions in task space. We describe the training, exploration and the control architecture of the systems as well as present an evaluation on the KUKA Light-Weight Robot. Our results show that the learned model solves the redundancy resolution problem under the given constraints with sufficient accuracy and generalizes to generate valid joint-space trajectories even in untrained areas of the workspace.

Recurrence enhances the spatial encoding of static inputs in reservoir networks

We shed light on the key ingredients of reservoir computing and analyze the contribution of the network dynamics to the spatial encoding of inputs. Therefore, we introduce attractor-based reservoir networks for processing of static patterns and compare their performance and encoding capabilities with a related feedforward approach. We show that the network dynamics improve the nonlinear encoding of inputs in the reservoir state which can increase the task-specific performance.

Assisted Gravity Compensation to cope with the complexity of kinesthetic teaching on redundant robots

Facilitating efficient programming-by-demonstration methods for advanced robot systems is an ongoing research challenge. This paper addresses one important challenge in this area, which is the programming of kinematically redundant robots. We argue that standard programming-by-demonstration methods for teaching task-space trajectories on a redundant robot using physical human-robot interaction are too complex for non-expert human tutors. We therefore introduce a new interaction and control concept for redundant robot systems, Assisted Gravity Compensation, based on a hierarchical control scheme, separating task-space programming from the redundancy resolution. The user is actively assisted by a given redundancy resolution while kinesthetically teaching task-space trajectories. This control scheme is implemented on our experimental robot system called FlexIRob and we briefly present results of a kinesthetic teaching experiment obtained in a larger field study on physical Human-Robot Interaction with 48 industrial workers. These results show, that the Assisted Gravity Compensation reduces the complexity of a kinesthetic teaching task, which is revealed by an improved task performance, making kinesthetic teaching an efficient programming-by-demonstration method for redundant robots.

Multi-directional continuous association with input-driven neural dynamics

We present an input-driven dynamical system approach to continuous association. Previous formulations of associative reservoir computing networks and associative extreme learning machines are unified and generalized to multiple modalities. Association in these networks proceeds by externally driving parts of the network. Through continuous variation of driving inputs, a continuous association of output patterns is achieved. Robust association in this scheme requires to cope with potential error amplification of feedback dynamics and to handle differently sized input and output modalities such that the outcome of association is controlled by the driving inputs. We propose a dendritic neuron model in combination with a regularization technique to address both issues. The presented method allows for tuning contributions from each modality to the hidden representation by prescribed factors while the regularization of network weights mitigates the problem of error amplification. The scalability of the approach to high-dimensional applications is demonstrated in image and audio processing scenarios.

Model-free path planning for redundant robots using sparse data from kinesthetic teaching

The paper addresses path planning for a redundant robot arm that is maneuvering in confined spaces, where neither an explicit model nor external perception of the possibly frequently changing environment is available. Our approach is rather solely based on data from kinesthetic demonstrations of feasible configurations provided by a user. The key challenge is to create a graph-based representation of the demonstrated free space incrementally and online by means of an specifically tailored instantaneous topological map at runtime. Subsequent application of standard graph-based planning in combination with a learned generalization of the demonstrated redundancy resolution then enables the robot to safely move in the realm of the demonstrated task space areas. This model-free approach greatly enhances configurability and flexibility of the robot for assistance applications, where movement capabilities need to be realized without explicit programming.