Dorothea Koert

Technische Universität Darmstadt | TU · Department of Computer Science (Dept.20)

Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling...

Introduction: In Interactive Task Learning (ITL), an agent learns a new task through natural interaction with a human instructor. Behavior Trees (BTs) offer a reactive, modular, and interpretable way of encoding task descriptions but have not yet been applied a lot in robotic ITL settings. Most existing approaches that learn a BT from human demonst...

Designing cooperative AI-systems that do not automate tasks but rather aid human cognition is challenging and requires human-centered design approaches. Here, we introduce AI-aided brainstorming for solving guesstimation problems, i.e. estimating quantities from incomplete information, as a testbed for human-AI interaction with large language model...

Human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical Human-Computer Interaction settings. Specifically, there is existing work indicating that it is beneficial f...

Interactive Reinforcement Learning (IRL) uses human input to improve learning speed and enable learning in more complex environments. Human action advice is here one of the input channels preferred by human users. However, many existing IRL approaches do not explicitly consider the possibility of inaccurate human action advice. Moreover, most appro...

Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative Human-Robot Interactions (HRI). Learning from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown promising results, especially when coup...

Interactive Reinforcement Learning (IRL) has shown promising results in decreasing the learning times of Reinforcement Learning algorithms by incorporating human feedback and advice. In particular, the integration of multimodal feedback channels such as speech and gestures into IRL systems can enable more versatile and natural interaction of everyd...

It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) have shown to be a promising framework to learn generalizable trajectory generators from distributions over demonstrated trajectories. However, in practical applications t...

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot...

The ability to learn new tasks by sequencing already known skills is an important requirement for future robots. Reinforcement learning is a powerful tool for this as it allows for a robot to learn and improve on how to combine skills for sequential tasks. However, in real robotic applications, the cost of sample collection and exploration prevent...

Intelligent assistive robots can potentially contribute to maintaining an elderly person’s independence by supporting everyday life activities. However, the number of different and personalized activities to be supported renders pre-programming of all respective robot behaviors prohibitively difficult. Instead, to cope with a continuous and potenti...

Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks given demonstrated trajectories. However, these approaches are not applicable when the demonstrations are suboptimal or when the generalization capabilities of the learned models cannot cope with the changes in the environment. Nevertheless, in real co...

In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to laymen and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) allows capturing tasks by learning robot trajectories from demonstrations including...

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalit...

Robot imitation based on observations of the hu- man movement is a challenging problem as the structure of the human demonstrator and the robot learner are usually different. A movement that can be demonstrated well by a human may not be kinematically feasible for robot reproduction. A common approach to solve this kinematic mapping is to retarget...

Participating in the RoboCup Rescue Real Robot League competition for approximately 5 years, the members of Team Hector Darmstadt have always focused on robot autonomy for Urban Search and Rescue (USAR). In 2014, the team won the RoboCup RRL competition. This marked the first time a team with a strong focus on autonomy won the championship. This pa...