Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified instructions, resulting in inefficient stop-and-replan behavior when corrections or clarifications occur. In this paper, we introduce a novel reasoning-based incremental parser which integrates an online motion planning algorithm within the cognitive architecture. Our approach enables continuous adaptation to dynamic linguistic input, allowing robots to update motion plans without restarting execution. The incremental parser maintains multiple candidate parses, leveraging reasoning mechanisms to resolve ambiguities and revise interpretations when needed. By combining symbolic reasoning with online motion planning, our system achieves greater flexibility in handling speech corrections and dynamically changing constraints. We evaluate our framework in real-world human-robot interaction scenarios, demonstrating online adaptions of goal poses, constraints, or task objectives. Our results highlight the advantages of integrating incremental language understanding with real-time motion planning for natural and fluid human-robot collaboration. The experiments are demonstrated in the accompanying video at www.acin.tuwien.ac.at/42d5.

Learning from demonstration (LfD) has emerged as a promising approach enabling robots to acquire complex tasks directly from human demonstrations. However, tasks involving surface interactions on freeform 3D surfaces present unique challenges in modeling and execution, especially when geometric variations exist between demonstrations and robot execution. This paper proposes a novel framework called probabilistic surface interaction primitives (ProSIP), which systematically incorporates the surface path and the local surface features into the learning procedure. An instrumented tool allows seamless recording and execution of human demonstrations. By design, ProSIPs are independent of time, invariant to rigid-body displacements, and apply to any robotic platform with a Cartesian controller. The framework is employed for an edge-cleaning task of bathroom sinks. The generalization capability to various object geometries and significantly distorted objects is demonstrated. Simulations and an experimental setup with a 9-degrees-of-freedom robotic platform confirm the performance.

This work proposes a highly parallelized optimization scheme to simultaneously optimize the robot base and tool center point (TCP) placement within a robotic work cell for a sequence of pick-and-place tasks. The placement is optimized for minimum cycle time by considering the scenario holistically, including point-to-point trajectory planning while respecting the kinodynamic constraints of the robot, collision avoidance in highly constrained environments, redundancy in grasp configurations and inverse kinematic solutions, and the cyclic constraint of the process. The proposed algorithm is applied to optimize the robot base and TCP placements in a spatially constrained packaging scenario, demonstrating a cycle time reduction of 41% compared to state-of-the-art approaches. The results are validated experimentally using a KUKA LBR iiwa with 7 degrees of freedom, where the TCP placement is realized using topology optimization and 3D printing.

Real-time applications for autonomous operations depend largely on fast and robust vision-based localization systems. Since image processing tasks require processing large amounts of data, the computational resources often limit the performance of other processes. To overcome this limitation, traditional marker-based localization systems are widely used since they are easy to integrate and achieve reliable accuracy. However, classical marker-based localization systems significantly depend on standard cameras with low frame rates, which often lack accuracy due to motion blur. In contrast, event-based cameras provide high temporal resolution and a high dynamic range, which can be utilized for fast localization tasks, even under challenging visual conditions. This paper proposes a simple but effective event-based pose estimation system using active LED markers (ALM) for fast and accurate pose estimation. The proposed algorithm is able to operate in real time with a latency below 0.5 ms while maintaining output rates of 3 kHz. Experimental results in static and dynamic scenarios are presented to demonstrate the performance of the proposed approach in terms of computational speed and absolute accuracy, using the OptiTrack system as the basis for measurement. Moreover, we demonstrate the feasibility of the proposed approach by deploying the hardware, i.e., the event-based camera and ALM, and the software in a real quadcopter application. Our project page is available at: almpose.github.io

Robots need to be as simple to use as tools in a workshop and allow non-experts to program, modify and execute tasks. In particular for repetitive tasks in high-mix/low-volume production, robotic support and physical human-robot interaction (pHRI) help to significantly increase productivity. In path-following control (PFC), the geometric description of the path is decoupled from the time evolution of the robot's end-effector along the path. PFC is inherently suitable for pHRI since path progress can be derived from the interaction with the human. In this work, an extension to multi-path PFC is proposed, which allows smooth transitions between the paths initiated by the human. Additionally, two pHRI modes called path snap-in and orientation snap-in are proposed, which use attractive forces to snap the robot end-effector onto a path or a predefined orientation. Moreover, the stability properties of PFC are inherited and the method is applicable to linear, nonlinear and self-intersecting paths. The proposed pHRI modes are validated on an experimental drilling task for teach-in (using orientation snap-in) and execution (using path snap-in) with the kinematically redundant collaborative robot Kuka Lbr iiwa 14 R820.

Schmitt-Triggers (S/Ts) are often utilized to clean noisy analog signals at intermediate voltage values in digital circuits. However, they are vulnerable to metastability, which may cause the same undesired non-digital output behavior that was supposed to be removed in the first place. To enable an efficient characterization of static and dynamic metastability properties of S/Ts (e.g., the metastable voltages, the resolution time constants and the overall total resolution times), this work introduces multiple simulation approaches based on control theory, AC, DC and transient analyses. The accuracy and runtime of all methods are compared and discussed by applying them to an analytically describable idealized circuit model as well as three common circuit implementations. Altogether, this work represents a comprehensive resource for investigating the metastable behavior in S/Ts. Even more, the proposed methods are applicable beyond the S/T, enabling an efficient characterization of static and dynamic metastable behavior in general circuits as well.

In this paper, the experimental swing-up of a spherical pendulum mounted on a collaborative robot is presented. The complete mechanical system consists of nine degrees of freedom (DoFs). The primary focus of this work is the design of a fast trajectory planning for the swing-up by systematically incorporating the kinematic and dynamics constraints. The proposed algorithm consists of two steps: First, an offline trajectory optimization is used to build a database of swing-up trajectories, with an average computing time of 10 s for one trajectory. Second, a fast trajectory replanner based on a constrained quadratic program is described, which computes the swing-up trajectory for an arbitrary initial configuration of the system with an average computing time of 0.2 s. Simulations and experimental results demonstrate the swing-up of the spherical pendulum using a discrete time-variant linear quadratic regulator as a feedback controller.

The robot base placement of an industrial robot in flexible production lines is crucial due to the limited workspace of robots, in particular for complex continuous paths that change frequently. Costly and time-consuming repositioning of the robot can be avoided by merely adapting the tool center point (TCP) of the robot, which is the focus of this work. To this end, an algorithm for the optimal TCP placement for a set of tool paths is proposed. This algorithm is based on a fast joint-space path planner which is capable of moving through kinematic singularities and takes into account wide turning ranges of individual robot axes. Furthermore, the proposed concept also applies to the optimal robot base placement. The feasibility of the approach is demonstrated for a trim application in shoe production for a set of 44 complex continuous tool paths.

In this article, a novel approach for the versatile wrinkle-free application of (curved) precut adhesive tapes on freeform 3-D surfaces is presented. Straight and curved tape application paths are mapped onto the 3-D object as geodesics and as lines with imposed geodesic curvature, respectively. The proposed surface-based path following control concept extends the classical path following control by a novel parallel contact frame and a parallel projection operator. Using a static state feedback, the robotic system is transformed into a system with linear input-output behavior in the path coordinates. This allows to traverse a path on a 3-D object with a draping roll without turning around the surface normal vector. The latter prevents distortions and wrinkles of the applied tape. Experimental results with a Kuka LBR iiwa 14 R820 demonstrate the feasibility of the proposed approach.