Robert Krug

That’s on my mind! robot to human intention communication through on-board projection on shared floor space

The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robots ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot.

On the efficient computation of independent contact regions for force closure grasps

Since the introduction of independent contact regions in order to compensate for shortcomings in the positioning accuracy of robotic hands, alternative methods for their generation have been proposed. Due to the fact that (in general) such regions are not unique, the computation methods used usually reflect the envisioned application and/or underlying assumptions made. This paper introduces a parallelizable algorithm for the efficient computation of independent contact regions, under the assumption that a user input in the form of initial guess for the grasping points is readily available. The proposed approach works on discretized 3D-objects with any number of contacts and can be used with any of the following models: frictionless point contact, point contact with friction and soft finger contact. An example of the computation of independent contact regions comprising a non-trivial task wrench space is given.

Model Predictive Motion Control based on Generalized Dynamical Movement Primitives

In this work, experimental data is used to estimate the free parameters of dynamical systems intended to model motion profiles for a robotic system. The corresponding regression problem is formed as a constrained non-linear least squares problem. In our method, motions are generated via embedded optimization by combining dynamical movement primitives in a locally optimal way at each time step. Based on this concept, we introduce a model predictive control scheme which allows generalization over multiple encoded behaviors depending on the current position in the state space, while leveraging the ability to explicitly account for state constraints to the fulfillment of additional tasks such as obstacle avoidance. We present a numerical evaluation of our approach and a preliminary verification by generating grasping motions for the anthropomorphic Shadow Robot hand/arm platform.

No More Heavy Lifting: Robotic Solutions to the Container Unloading Problem

This article discusses the scientifically and industrially important problem of automating the process of unloading goods from standard shipping containers. We outline some of the challenges barring further adoption of robotic solutions to this problem, ranging from handling a vast variety of shapes, sizes, weights, appearances, and packing arrangements of the goods, through hard demands on unloading speed and reliability, to ensuring that fragile goods are not damaged. We propose a modular and reconfigurable software framework in an attempt to efficiently address some of these challenges. We also outline the general framework design and the basic functionality of the core modules developed. We present two instantiations of the software system on two different fully integrated demonstrators: (1) coping with an industrial scenario, i.e., the automated unloading of coffee sacks with an already economically interesting performance; and (2) a scenario used to demonstrate the capabilities of our scientific and technological developments in the context of medium- to long-term prospects of automation in logistics. We performed evaluations that allowed us to summarize several important lessons learned and to identify future directions of research on autonomous robots for the handling of goods in logistics applications.

Representing movement primitives as implicit dynamical systems learned from multiple demonstrations

This work deals with the problem of parameter estimation of dynamical systems intended to model demonstrated motion profiles for a system of interest. The regression problem is formulated as a constrained nonlinear least squares problem. We present an approach that extends the concept of dynamical movement primitives to account for multiple demonstrations of a motion. We maintain an implicit dynamical system that resembles the demonstrated trajectories in a locally optimal way. This is achieved by solving a quadratic program (that encodes our notion of resemblance) at each sampling time step. Our method guarantees predictable state evolution even in regions of the state space not covered by the demonstrations.

The Next Step in Robot Commissioning: Autonomous Picking and Palletizing

So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 at a success rate of 94.7. Our system is able to autonomously carry out simple order picking tasks in a human-safe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

Independent Contact Regions based on a patch contact model

The synthesis of multi-fingered grasps on nontrivial objects requires a realistic representation of the contact between the fingers of a robotic hand and an object. In this work, we use a patch contact model to approximate the contact between a rigid object and a deformable anthropomorphic finger. This contact model is utilized in the computation of Independent Contact Regions (ICRs) that have been proposed as a way to compensate for shortcomings in the finger positioning accuracy of robotic grasping devices. We extend the ICR algorithm to account for the patch contact model and show the benefits of this solution.

Analytic grasp success prediction with tactile feedback

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

Grasp quality evaluation done right: How assumed contact force bounds affect Wrench-based quality metrics

Wrench-based quality metrics play an important role in many applications such as grasp planning or grasp success prediction. In this work, we study the following discrepancy which is frequently overlooked in practice: the quality metrics are commonly computed under the assumption of sum-magnitude bounded contact forces, but the corresponding grasps are executed by a fully actuated device where the contact forces are limited independently. By means of experiments carried out in simulation and on real hardware, we show that in this setting the values of these metrics are severely underestimated. This can lead to erroneous conclusions regarding the actual capabilities of the grasps under consideration. Our findings highlight the importance of matching the physical properties of the task and the grasping device with the chosen quality metrics.

Generation of Independent Contact Regions on objects reconstructed from noisy real-world range data

The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.