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Human-robot cooperative object manipulation with contact changes

Gienger, Michael (author), Ruiken, Dirk (author), Bates, T. (author), Regaieg, Mohamed (author), Meibner, M. (author), Kober, J. (author), Seiwald, Philipp (author), Hildebrandt, Arne Christoph (author)

This paper presents a system for cooperatively manipulating large objects between a human and a robot. This physical interaction system is designed to handle, transport, or manipulate large objects of different shapes in cooperation with a human. Unique points are the bi-manual physical cooperation, the sequential characteristic of the...

conference paper 2018

document

Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation

Calli, B. (author), Caarls, W. (author), Wisse, M. (author), Jonker, P.P. (author)

In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task...

journal article 2018

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A topology of shared control systems: Finding common ground in diversity

Abbink, D.A. (author), Carlson, Tom (author), Mulder, M. (author), de Winter, J.C.F. (author), Aminravan, Farzad (author), Gibo, T.L. (author), Boer, E.R. (author)

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of...

journal article 2018

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Deep Learning and Machine Learning in Robotics

Bonsignorio, Fabio (author), Hsu, David (author), Johnson-Roberson, Matthew (author), Kober, J. (author)

Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...

contribution to periodical 2020

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Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback

Pérez-Dattari, Rodrigo (author), Celemin, Carlos (author), Franzese, G. (author), Ruiz-del-Solar, Javier (author), Kober, J. (author)

Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and...

journal article 2020

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Haptic training: Which types facilitate (re)learning of which motor task and for whom Answers by a review

Basalp, Ekin (author), Wolf, Peter (author), Marchal Crespo, L. (author)

The use of robots has attracted researchers to design numerous haptic training methods to support motor learning. However, investigations of new methods yielded inconclusive results regarding their effectiveness to enhance learning due to the diversity of tasks, haptic designs, participants skill level, and study protocols. In this review, we...

review 2021

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Efficient and Goal-Directed Oscillations in Articulated Soft Robots: the Point-to-Point Case

Bonacchi, Luigi Bono (author), Roa, Maximo A. (author), Sesselmann, Anna (author), Loeffl, Florian (author), Albu-Schaffer, Alin (author), Della Santina, C. (author)

Introducing elasticity in the mechanical design can endow robots with the ability of performing efficient and effective periodic motions. Yet, devising controllers that can take advantage of such elasticity is still an open challenge. This letter tackles an instance of this general problem, by proposing a control architecture for executing...

journal article 2021

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Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Chen, Zhe (author), Alonso-Mora, J. (author), Bai, X. (author), Harabor, Daniel Damir (author), Stuckey, Peter James (author)

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is...

journal article 2021

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Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

Kulhanek, Jonas (author), Derner, Erik (author), Babuska, R. (author)

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment....

journal article 2021

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Contour Moments Based Manipulation of Composite Rigid-Deformable Objects With Finite Time Model Estimation and Shape/Position Control

Qi, Jiaming (author), Ma, Guangfu (author), Zhu, J. (author), Zhou, Peng (author), Lyu, Yueyong (author), Zhang, Haibo (author), Navarro-Alarcon, David (author)

The robotic manipulation of composite rigid-deformable objects (i.e., those with mixed nonhomogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this article, we propose a...

journal article 2022

document

Mean Field Behavior of Collaborative Multiagent Foragers

Jarne Ornia, D. (author), Zufiria, Pedro J. (author), Mazo, M. (author)

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain...

journal article 2022

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Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data

Rist, C.B. (author), Emmerichs, David (author), Enzweiler, Markus (author), Gavrila, D. (author)

Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real-world data that is sparse and occluded. We propose a scene segmentation network based on local Deep Implicit Functions as a novel learning-based method for scene...

journal article 2022

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Challenges and Outlook in Robotic Manipulation of Deformable Objects

Zhu, J. (author), Cherubini, Andrea (author), Dune, Claire (author), Navarro-Alarcon, David (author), Alambeigi, Farshid (author), Berenson, Dmitry (author), Ficuciello, Fanny (author), Harada, Kensuke (author), Kober, J. (author), Yuan, Wenzhen (author)

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and health-care sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is...

journal article 2022

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Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Knödler, L. (author), Salmi, C. (author), Zhu, H. (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

journal article 2022

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Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention

Wenk, Nicolas (author), Jordi, Mirjam V. (author), Buetler, Karin A. (author), Marchal Crespo, L. (author)

Combining immersive virtual reality (VR) using head-mounted displays (HMDs) with assisting robotic devices might be a promising procedure to enhance neurorehabilitation. However, it is still an open question how immersive virtual environments (VE) should be designed when interacting with rehabilitation robots. In conventional training, the...

journal article 2022

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Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations

Mészáros, A. (author), Franzese, G. (author), Kober, J. (author)

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement,...

journal article 2022

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Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

Cai, Yifan (author), Dahiya, Abhinav (author), Wilde, N. (author), Smith, Stephen L. (author)

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at...

conference paper 2022

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Energy-Efficient Routing of a Multirobot Station: A Flexible Time-Space Network Approach

Xin, Jianbin (author), Meng, Chuang (author), D'Ariano, Andrea (author), Schulte, F. (author), Peng, Jinzhu (author), Negenborn, R.R. (author)

This paper investigates a novel routing problem of a multi-robot station in a manufacturing cell. In the existing literature, the objective is to minimize the cycle time or energy consumption separately. The routing problem considered in this paper aims to reduce the cycle time and energy consumption jointly for each robot while avoiding...

journal article 2023

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Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Xin, Jianbin (author), Wu, Xuwen (author), D'Ariano, Andrea (author), Negenborn, R.R. (author), Zhang, Fangfang (author)

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed...

journal article 2023

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Dynamic Optimization Fabrics for Motion Generation

Spahn, M. (author), Wisse, M. (author), Alonso-Mora, J. (author)

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show...

journal article 2023