Günter Niemeyer

High-performance robotic muscles from conductive nylon sewing thread

Natural muscles exhibit high power-to-weight ratios, inherent compliance and damping, fast actuation and high dynamic ranges. Unfortunately, traditional robotic actuators have been unable to attain similar properties, especially in a slender muscle-like form factor. Recently, super-coiled polymer (SCP) actuators have rejuvenated the promise of an artificial muscle. Constructed from commercial nylon fishing line or sewing thread and twisted until coils form, these lightweight actuators have been shown to produce significant mechanical power when thermally cycled. In this paper, we develop a thermomechanical and thermoelectric model of SCP actuators, and examine their controllability. With off-the-shelf conductive sewing thread, we show the ability to produce controlled forces in under 30 ms, exceeding human muscle performance. Finally, we use SCP actuators in a robotic hand to demonstrate their applicability as a low-cost, high performance robotic muscle.

Antagonistic muscle based robot control for physical interactions

Robots are ever more present in human environments and effective physical human-robot interactions are essential to many applications. But to a person, these interactions rarely feel biological or equivalent to a human-human interactions. It is our goal to make robots feel more human-like, in the hopes of allowing more natural human-robot interactions. In this paper, we examine a novel biologically-inspired control method, emulating antagonistic muscle pairs based on a nonlinear Hill model. The controller captures the muscle properties and dynamics and is driven solely by muscle activation levels. A human-robot experiment compares this approach to PD and PID controllers with equivalent impedances as well as to direct human-human interactions. The results show the promise of driving motors like muscles and allowing users to experience robots much like humans.

Evaluating Social Perception of Human-to-Robot Handovers Using the Robot Social Attributes Scale (RoSAS)

This work explores social perceptions of robots within the domain of human-to-robot handovers. Using the Robotic Social Attributes Scale (RoSAS), we explore how users socially judge robot receivers as three factors are varied: initial position of the robot arm prior to handover, grasp method employed by the robot when receiving a handover object trading off perceived object safety for time efficiency, and retraction speed of the arm following handover. Our results show that over multiple handover interactions with the robot, users gradually perceive the robot receiver as being less discomforting and having more emotional warmth. Additionally, we have found that by varying grasp method and retraction speed, users may hold significantly different judgments of robot competence and discomfort. With these results, we recognize empirically that users are able to develop social perceptions of robots which can change through modification of robot receiving behaviour and through repeated interaction with the robot. More widely, this work suggests that measurement of user social perceptions should play a larger role in the design and evaluation of human-robot interactions and that the RoSAS can serve as a standardized tool in this regard.

Toward controlling a KUKA LBR IIWA for interactive tracking

In this paper we use KUKAs Fast Robot Interface (FRI) to design and implement a tracking controller on the Lightweight Robot (LBR) IIWA. We seek low latency, accurate and smooth tracking of the link positions to facilitate human interaction tasks. Focusing on a single joint and its low-level series elastic dynamics, we identify the internal torque control structure and its characteristics. Tracking controllers of varying complexity are tested in an optical motion capture system to provide an independent external reference measurement. Using full state feedback of both motor position and sensed joint torque, we achieve smooth and good tracking of the unsensed link positions.

Catching a real ball in virtual reality

We present a system enabling users to accurately catch a real ball while immersed in a virtual reality environment. We examine three visualizations: rendering a matching virtual ball, the predicted trajectory of the ball, and a target catching point lying on the predicted trajectory. In our demonstration system, we track the projectile motion of a ball as it is being tossed between users. Using Unscented Kalman Filtering, we generate predictive estimates of the balls motion as it approaches the catcher. The predictive assistance visualizations effectively increases the users senses but can also alter the users strategy in catching.

Absolutely stable model-based 2-port force controller for telerobotic applications

Large industrial-like slave robots pose a challenging problem for telerobotic control designers focused on achieving good transparency. The human operator typically feels both the large friction forces and heavy inertial forces inherent to these robots. Force control can be used to attempt and hide these internal forces from the user, but force control is a challenging design problem, especially in situations like telerobotics where it is not clear what the environmental impedance will be at any given moment. This paper introduces a model-based force controller designed to reject the friction in slave robots to improve the overall transparency of the telerobotic system. The primary objective of the force controller is to ensure the closed loop slave 2-port is absolutely stable such that the controller maintains the robustness of the overall telerobot. An analysis is provided that shows that, in order to achieve absolute stability, a local slave-side force controller cannot hide any of the robot’s inertial forces. It is from this result that model-based force control gets its name as it uses a model of the robot’s inertial properties to reject friction forces without attempting to reject inertial forces.

Active vertical stabilization mechanism for lightweight handheld cameras

Camera technology is continuously improving and high quality cameras are now available under one pound of weight. This enables novel and innovative uses, for example at the end of a long boom pole. Unfortunately lighter cameras used in such ways are more susceptible to vertical disturbances and the bouncing associated with walking resulting in shaking and distortion. We introduce a miniaturized active stabilization mechanism that attenuates such disturbances and keeps the camera steady. Feedback control effectively emulates the stabilizing inertial dynamics associated with higher weights without the penalty of higher weight. The system uses only accelerometer readings and avoids pure integration and associated numerical drift issues. We design, analyze, build, and test the mechanism to show appropriate performance.