In robotics, many control and planning schemes have been developed to ensure human physical safety in human-robot interaction. The human psychological state and the expectation towards the robot, however, are typically neglected. Even if the robot behaviour is regarded as biomechanically safe, humans may still react with a rapid involuntary motion (IM) caused by a startle or surprise. Such sudden, uncontrolled motions can jeopardize safety and should be prevented by any means. In this letter, we propose the Expectable Motion Unit (EMU), which ensures that a certain probability of IM occurrence is not exceeded in a typical HRI setting. Based on a model of IM occurrence generated through an experiment with 29 participants, we establish the mapping between robot velocity, robot-human distance, and the relative frequency of IM occurrence. This mapping is processed towards a real-time capable robot motion generator that limits the robot velocity during task execution if necessary. The EMU is combined in a holistic safety framework that integrates both the physical and psychological safety knowledge. A validation experiment showed that the EMU successfully avoids human IM in five out of six cases.

Elastic actuation taps into elastic elements' energy storage for dynamic motions beyond rigid actuation. While Series Elastic Actuators (SEA) and Variable Stiffness Actuators (VSA) are highly sophisticated, they do not fully provide control over energy transfer timing. To overcome this problem on the basic system level, the Bi-Stiffness Actuation (BSA) concept was recently proposed. Theoretically, it allows for full link decoupling, while simultaneously being able to lock the spring in the drive train via a switch-and-hold mechanism. Thus, the user would be in full control of the potential energy storage and release timing. In this work, we introduce an initial proof-of-concept of Bi-Stiffness-Actuation in the form of a 1-DoF physical prototype, which is implemented using a modular testbed. We present a hybrid system model, as well as the mechatronic implementation of the actuator. We corroborate the feasibility of the concept by conducting a series of hardware experiments using an open-loop control signal obtained by trajectory optimization. Here, we compare the performance of the prototype with a comparable SEA implementation. We show that BSA outperforms SEA 1) in terms of maximum velocity at low final times and 2) in terms of the movement strategy itself: The clutch mechanism allows the BSA to generate consistent launch sequences while the SEA has to rely on lengthy and possibly dangerous oscillatory swing-up motions. Furthermore, we demonstrate that providing full control authority over the energy transfer timing and link decoupling allows the user to synchronously release both elastic joint and gravitational energy. This facilitates the optimal exploitation of elastic and gravitational potentials in a synergistic manner.

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings.

Tomorrow, Prof. Haddadin from the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich will explain live to students the opportunities and limitations of robots. Not without bringing GARMI, of course: He can blink, wave, wait tables and even do rehabilitation exercises with patients as a physiotherapist's assistant. German You will find the broadcast afterward in the ZDF Mediathek - https://lnkd.in/eENS7Hvq

Sami Haddadin runs a'robot kindergarten' where intelligent machines learn from each other.Credit: Technical University of Munich "AI and robotics development pull us right into the heart of what it is to be human," says Sami Haddadin, founding director of the Munich School of Robotics and Machine Intelligence (MSRM) at TUM. "We're not looking to usher in an'age of automatons'. Rather, we hope to enable a smooth transition to an age of human- machine interaction." MSRM's research agenda covers the understanding of humans in order to develop intelligent machines that can, in turn, help humans. Haddadin gives an example: give a young child a key and, within around 20 tries, they can unlock a door. A child's intuitive ability to manipulate a tool is one aspect, but they also watch and learn from adults.

Industrial robots get lots done, but they're expensive, dangerous and hard to program. That's why roboticists are turning to cobots - collaborative robots made to work alongside us and, perhaps one day, in our homes. "We are trying to make a support-service robot for people who can't support themselves or leave home," explains Simon Haddadin, co-founder of Munich-based Franka Emika. The lightweight, three-kilogram frames of Franka Emika's cobots mean they can safely work in the same building as humans, and will stop automatically if they come into close contact with one. "It basically means it has a sense of touch along the entire structure," Haddadin says.

Sami Haddadin once attached a knife to a robot manipulator and programmed it to impale his arm. He was demonstrating how a new force-sensing control scheme he designed was able to detect the contact and instantly stop the robot, as it did. Now Haddadin wants to make that same kind of safety feature, which has long been limited to highly sophisticated and expensive systems, affordable to anyone using robots around people. Sometime in 2017, his Munich-based startup, Franka Emika, will start shipping a rather remarkable robotic arm. It's designed to be easy to set up and program, which is nice.

Sami Haddadin once attached a knife to a robot manipulator and programmed it to impale his arm. He was demonstrating how a new force-sensing control scheme he designed was able to detect the contact and instantly stop the robot, as it did. Now Haddadin wants to make that same kind of safety feature, which has long been limited to highly sophisticated and expensive systems, affordable to anyone using robots around people. Sometime in 2017, his Munich-based startup, Franka Emika, will start shipping a rather remarkable robotic arm. It's designed to be easy to set up and program, which is nice.