35 accepted papers
Human collaboration with robots requires flexible role adaptation, enabling the robot to switch between an active leader and a passive follower. Effective role switching depends on accurately estimating human intentions, which is typically achieved through external force analysis, nominal robot dyna
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction is a fundamental and crucial requirement for the safety and admissibility of planned trajectories on various systems. M…
In this paper, we address a practical distributed Bayesian learning problem with asynchronous measurements and predictions due to diverse computational conditions. To this end, asynchronous distributed Gaussian process (AsyncDGP) regression is proposed, which is the first effective online distribute…
We propose a family of Gaussian processes (GP) for dynamical systems with linear time-invariant responses, which are nonlinear only in initial conditions. This linearity allows us to tractably quantify forecasting and representational uncertainty, simultaneously alleviating the challenge of computin…
The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data. While the geometrical properties of such certificate functions are well understood, synthesizing them using machine le…
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typically by leveraging model error bounds to specify robust constraint-based safety filters. However, if the learned model un…
Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations, especially in learning pipelines involving, for example, artificial neural networks. In this paper, we investigate rei
A drawing robot avatar is a robotic system that allows for telepresence-based drawing, enabling users to remotely control a robotic arm and create drawings in real-time from a remote location. The proposed control framework aims to improve bimanual robot telepresence quality by reducing the user wor…
In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end processors, analyzing stability and designing a stable controller present significant challenges. This paper distinguishes…
Ensuring safety and adapting to the user’s behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot’s mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the inte…
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specif…
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e.g., the state of an agent or the reward of a policy. Forecasts of such complex phenomena are commonly described by…
A main challenge restricting the application of control barrier functions (CBFs) to complex scenarios is the absence of robustness against uncertainties induced by both measurements of the environment and robot dynamics. In this letter, we propose an uncertainty-aware, learning-based approach to con
While Gaussian processes are a mainstay for various engineering and scientific applications, the uncertainty estimates don't satisfy frequentist guarantees and can be miscalibrated in practice. State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posteri…
For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle representations and Gaussian uncertainty, which can be inaccurate. While visual perception can deliver a more accurate
Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify risk. However, state-of-the-art techniques for safety-critical settings hinge on the assumption that the kernel hyperparame…
Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Unmanned Aerial Vehicles (UAVs), which are described…
The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its hig…
Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of their model error. Gaussian processes provide such a measure and uniform error bounds have been derived, which allow safe c…
This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot w…
Programming by Demonstration allows to transfer skills from human demonstrators to robotic systems by observation and reproduction. One aspect that is often overlooked is that humans show different trajectories over multiple demonstrations for the same task. Observed movements may be more precise in…
In this letter, we address the modeling and learning of complex nonlinear rigid-body motions employing Gaussian processes. As the common procedure of using Euler angles in the Gaussian process results in inaccurate predictions for large rotations, we represent the input data by axis-angle pseudovect
Knowing accurately the dynamic parameters of a manipulated object is required for common coordination strategies in physical human-robot interaction. Bias in object dynamics results in inaccurately calculated robot wrenches, which may disturb the human during interaction and bias the recognition of…
A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e.g., from first principles in physics. However, meta-knowledge on the system’s behavior is often given and should be exploited: Stabil…
In this paper we consider the problem in which the human commands the overall behavior of a robot team while the robots are controlled to comply with formation constraints. Such human-robot team interaction is challenging in terms of system complexity and control synthesis. Port-Hamiltonian framewor…
Robot teams require planning and adaptive capabilities in order to perform cooperative manipulation tasks in dynamic or unstructured environments. Since these capabilities are inherent to humans, it is suitable to consider human-robot team teleoperation for cooperative manipulation where a single hu…
Many robotic applications, especially if humans are involved, require the robot to adhere to certain joint, workspace, velocity or force limits while simultaneously executing a task. In this paper, we introduce a control structure, which merges an arbitrary desired robot behavior with given constrai…
Dynamic Movement Primitives (DMPs) represent stable goal-directed or periodic movements, which are learned from observations or demonstrations. They rely on proper function approximators, which are sufficiently flexible to represent arbitrary movements but also ensure goal convergence in point-to-po…
For seamless physical human-robot interaction (pHRI), estimating human intention is essential. Most system identification approaches to pHRI model the human as a black box without prior assumptions about the underlying behavioral structure. However, integrating a priori knowledge about behavioral ch
In human-robot interaction (HRI) and especially in close or physical interaction, it is essential to ensure the human's safety. This is achieved by introducing virtual constraints defining a region, in which the robot is allowed to move safely. These safety regions may change over time during human-…
In cooperative manipulation tasks, load allocation is a crucial step in order to solve the intrinsic redundancy of the system. The desired wrench needs to be suitably distributed between the end-effectors to implement the desired motion of the manipulated object. In this framework, both the grasp ki…
Knowledge of the human grasp pose is crucial in common control schemes for human-robot object manipulation tasks. Biased estimates of the grasp pose cause undesired interaction wrenches on the human partner, which disturbs the interaction and the recognition of motion intention. A use of wearable mo…
The interaction of a single human with a team of cooperative robots, which collaboratively manipulate an object, poses a great challenge by means of the numerous possibilities of issuing commands to the team or providing appropriate feedback to the human. In this paper we propose a formation-based a…
This paper discusses an online dynamic motion generation scheme for nonprehensile object manipulation by using a set of predefined motions and a trajectory deformation algorithm capable of incorporating positional and velocity boundary constraints. By creating optimal trajectories offline and deform…
As the application of probabilistic models in robotic applications increases, the necessity of a systematic robot-control method that considers the effects of multiple uncertainty sources becomes more evident. Motivated by human sensorimotor findings, in this work we study the stochastic locally opt…