Many robotic tasks require fast and accurate force sensing capabilities to ensure adaptive behavior execution. While dedicated force-torque (FT) sensors are a common option, such devices induce extra costs, need additional power supply, and add weight to otherwise light-weight robotic systems. This paper presents a machine learning approach for estimating external forces acting on a robot based on common internal sensors only. In the training phase, a behavior-specific proprioceptive model is learned as compact representation of the expected proprioceptive feedback during task execution. First, the proprioceptive sensors relevant for the given behavior are identified using information-theoretic measures. Then, the proprioceptive model is learned using deep learning techniques. During behavior execution, the proprioceptive model is applied to actual sensor readings for estimation of external forces. Experiments performed with the UR5 robot demonstrate the ability for fast and accurate force estimation even in situations where a dedicated commercial FT sensor is not applicable.

Underground mines are a dangerous working environment and, therefore, robots could help putting less humans at risk. Traditional robots, sensors, and software often do not work reliably underground due to the harsh environment. This paper analyzes requirements and presents a robot design capable of navigating autonomously underground and manipulating objects with a robotic arm. The robot's base is a robust four wheeled platform powered by electric motors and able to withstand the harsh environment. It is equipped with color and depth cameras, lighting, laser scanners, an inertial measurement unit, and a robotic arm. We conducted two experiments testing mapping and autonomous navigation. Mapping a 75 meters long route including a loop closure results in a map that qualitatively matches the original map to a good extent. Testing autonomous driving on a previously created map of a second, straight, 150 meters long route was also successful. However, without loop closure, rotation errors cause apparent deviations in the created map. These first experiments showed the robot's operability underground.

We present a data-driven imitation learning system for learning human-robot interactions from human-human demonstrations. During training, the movements of two interaction partners are recorded through motion capture and an interaction model is learned. At runtime, the interaction model is used to continuously adapt the robot's motion, both spatially and temporally, to the movements of the human interaction partner. We show the effectiveness of the approach on complex, sequential tasks by presenting two applications involving collaborative human-robot assembly. Experiments with varied object hand-over positions and task execution speeds confirm the capabilities for spatio-temporal adaption of the demonstrated behavior to the current situation.

Robotic manipulation tasks often require the control of forces and torques exerted on external objects. This paper presents a machine learning approach for estimating forces when no force sensors are present on the robot platform. In the training phase, the robot executes the desired manipulation tasks under controlled conditions with systematically varied parameter sets. All internal sensor data, in the presented case from more than 100 sensors, as well as the force exerted by the robot are recorded. Using Transfer Entropy, a statistical model is learned that identifies the subset of sensors relevant for torque estimation in the given task. At runtime, the model is used to accurately estimate the torques exerted during manipulations of the demonstrated kind. The feasibility of the approach is shown in a setting where a robotic manipulator operates a torque wrench to fasten a screw nut. Torque estimates with an accuracy of well below ±1Nm are achieved. A strength of the presented model is that no prior knowledge of the robot's kinematics, mass distribution or sensor instrumentation is required.

In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a force-torque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware.

Abstract Interaction meshes are a promising approach for generating natural behaviors of virtual characters during ongoing user interactions. In this paper, we propose several extensions to the interaction mesh approach based on statistical analyses of the underlying example interactions. By applying principal component analysis and correlation analysis in addition to joint distance calculations, both the interaction mesh topology and the constraints used for mesh optimization can be generated in an automated fashion that accounts for the spatial and temporal contexts of the interaction. Copyright © 2015 John Wiley & Sons, Ltd.