Searched for: subject%3A%22Learn%255C%2Bfrom%255C%2Bdemonstrations%22
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Meccanici, Floris (author), Karageorgos, Dimitrios (author), Heemskerk, Cock J.M. (author), Abbink, D.A. (author), Peternel, L. (author)
Daily household tasks involve manipulation in cluttered and unpredictable environments and service robots require complex skills and adaptability to perform such tasks. To this end, we developed a teleoperated online learning approach with a novel skill refinement method, where the operator can make refinements to the initially trained skill...
conference paper 2023
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Avaei, S. (author), van der Spaa, L.F. (author), Peternel, L. (author), Kober, J. (author)
Humans often demonstrate diverse behaviors due to their personal preferences, for instance, related to their individual execution style or personal margin for safety. In this paper, we consider the problem of integrating both path and velocity preferences into trajectory planning for robotic manipulators. We first learn reward functions that...
journal article 2023
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Sedlar, Jiri (author), Stepanova, Karla (author), Skoviera, Radoslav (author), Behrens, Jan K. (author), Tuna, Matus (author), Sejnova, Gabriela (author), Sivic, Josef (author), Babuska, R. (author)
This letter introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects, which is a common case in imitation learning...
journal article 2023
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Sibona, F. (author), Luijkx, J.D. (author), van der Heijden, D.S. (author), Ferranti, L. (author), Indri, Marina (author)
The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling...
conference paper 2023
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Sun, Jianyong (author)
Learning from Demonstration (LfD) aims to learn versatile skills from human demonstrations. The field has been gaining popularity since it facilitates transferring knowledge to robots without requiring much expert knowledge. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this,...
master thesis 2022
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van Beem, Marnix (author)
State-of-the-art object grasping with 7-DOF robotic manipulators requires joint configuration planning methods in order to provide position control of the end-effector. These motion planners are able to calculate a motion plan to execute a safe grasp, while taking environmental constraints into account. In human-robot...
master thesis 2022
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de Lange, Rudy (author)
This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a...
master thesis 2022
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Uitendaal, Sven (author)
While robots execute many tasks where physical interaction with the environment is required, it is still challenging to control a robot that deliberately makes contact at a non-zero velocity, especially with multiple contact points that are impacted simultaneously.<br/>When there is a mismatch between planned and actual impact time, the robot...
master thesis 2022
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Avaei, Armin (author)
Humans often demonstrate diverse behaviours due to their personal preferences, for instance related to their individual execution style or personal margin for safety. In this paper, we consider the problem of integrating such preferences into planning of trajectories for robotic manipulators. We first learn<br/>reward functions that represent...
master thesis 2021
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Arunmoli, Karthik Arvind (author)
Learning from demonstration is a technique where the robot learns directly from humans. It can be beneficial to learn from humans directly because humans can easily demonstrate complex behaviors without being experts in demonstrating required tasks. However, it can be challenging to gather large amounts of data from humans because humans often...
master thesis 2021
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TSAI, CHIA-YU (author)
Deformable objects manipulation (DOM) is largely considered an open problem in robotics. The complexity stems from the high degrees of freedom and nonlinear nature of the object configurations. In this thesis, we consider placing and flattening tasks for cloth-like objects. We propose a practical framework to place a cloth on a surface based on...
master thesis 2021
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Suresh Kumar, Lalith Keerthan (author)
In this thesis, we propose a method titled "Task Space Policy Learning (TaSPL)", a novel technique that learns a generalised task/state space policy, as opposed to learning a policy in state-action space, from interactive corrections in the observation space or from state only demonstration data. This task/state space policy enables the agent to...
master thesis 2021
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Hermans, Max (author)
The current ATC system is seen as the most significant limitation to coping with an increased air traffic density. Transitioning towards an ATC system with a high degree of automation is essential to cope with future traffic demand of the airspace. In recent studies, reinforcement learning has shown promising results automating Conflict...
master thesis 2021
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Meccanici, Floris (author)
The general approach to generate collision free motion in a constraint environment is to use path planners, which demand a known environment and potentially fail otherwise. Learning from Demonstration (LfD) can be used instead to teach the robot unknown parts of the environment, such as a goal deviation or an unforeseen obstacle. The general...
master thesis 2021
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Muench, C. (author), Oliehoek, F.A. (author), Gavrila, D. (author)
Modeling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behavior of a human agent is advantageous for modeling the behavior with Markov Decision Processes (MDPs). However, learning the rewards that determine...
journal article 2021
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Park, Shinkyu (author), Cáp, M. (author), Alonso Mora, J. (author), Ratti, Carlo (author), Rus, Daniela (author)
In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city's canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human...
journal article 2021
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Valletta, P. (author)
Interactive machine learning describes a collection of methodologies in which a human user actively participates in a novice agent’s learning process, through providing corrective or evaluate feedback or demonstrative actions. A primary assumption in these methods is that user input is at worst nearoptimal, however a realistic set of...
master thesis 2020
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Koomen, Lenard (author)
The combination of reinforcement learning and deep neural networks has the potential to train intelligent autonomous agents on high dimensional sensory inputs, with applications in flight control. However, the amount of samples needed by these methods is often too large to use real-world interaction. In this work, mirror-descent guided policy...
master thesis 2020
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Jauhri, Snehal (author)
Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require demonstration data (in the form of state-action labels) and in many scenarios, the demonstrations can be expensive to obtain or too complex for a...
master thesis 2020
document
Perez Dattari, R.J. (author), Celemin, Carlos (author), Ruiz-del-Solar, Javier (author), Kober, J. (author)
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN...
conference paper 2020
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