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Physical human-robot cooperation (pHRC) has the potential to combine human and robot strengths in a team that can achieve more than a human and a robot working on the task separately. However, how much of the potential can be realized depends on the quality of cooperation, in which awarenes of the partner’s intention and preferences plays an important role. Preferences tend to be highly personal, and additionally depend on the cooperation partner and the cooperation itself. They can be hard to define in terms a robot would understand, and may change over time. This thesis focuses on learning ‘useful models’ from observed behavior, to let our robot adapt its behavior to better match its human partner’s preferences, and thus improve the cooperation. The aim is to capture personalized approximate models of human preferences –how a person likes to do something– from very few interactive observations, providing only small amounts of imprecise data, such that the robot can use the model to improve each user’s comfort. First, we learn a model to predict and optimize the human ergonomics in a pHRC task, such that our robot can ropose a plan, for both the human and itself, to solve the task in a way that is more ergonomic for its human partner. However, people do not necessarily prefer to act ergonomically, nor do we want to impose on them what a robot thinks best. Therefore, next, we apply inverse reinforcement learning (IRL), to capture less restrictive preference models: 1) path and velocity preferences for motion planning, and 2) on a higher level of abstraction, which (grasp or motion) action to initiate for proactive physical support. For learning to take the correct action in cooperation, we developed the disagreement-aware variable impedance (DAVI) controller to smoothly transition between providing active guidance and allowing the human to demonstrate alternative behavior....., Dr. M. Gienger contributed significantly to the realization of the dissertation., Learning & Autonomous Control

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 represent the user path and velocity preferences from kinesthetic demonstration. We then optimize the trajectory in two steps, first the path and then the velocity, to produce trajectories that adhere to both task requirements and user preferences. We design a set of parameterized features that capture the fundamental preferences in a pick-and-place type of object transportation task, both in the shape and timing of the motion. We demonstrate that our method is capable of generalizing such preferences to new scenarios. We implement our algorithm on a Franka Emika 7-DoF robot arm and validate the functionality and flexibility of our approach in a user study. The results show that non-expert users are able to teach the robot their preferences with just a few iterations of feedback., Biomechatronics & Human-Machine Control, Human-Robot Interaction, Learning & Autonomous Control

In order to make the coexistence between humans and robots a reality, we must understand how they may cooperate more effectively. Modern robots, empowered with reliable controls and advanced machine learning reasoning can face this challenge. In this article, we presented a Disagreement- Aware Variable Impedance (DAVI) Controller, where the robot stiffness is regulated as a function of the perceived disagreement with the human cooperator. We tested the algorithm on a 7 DoF Franka Emika Panda robot performing the learning of a pick&place task with continuous adaptation of the goal location and the via-points with human interactive corrections, triggered by our proposed approach. A validation study was conducted with 5 users in order to understand the reliability of the method., Paper presented at the workshop "Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust" 23 May 2022, Learning & Autonomous Control

In order to make the coexistence between humans and robots a reality, we must understand how they may cooperate more effectively. Modern robots, empowered with reliable controls and advanced machine learning reasoning can face this challenge. In this article, we presented a Disagreement- Aware Variable Impedance (DAVI) Controller, where the robot stiffness is regulated as a function of the perceived disagreement with the human cooperator. We tested the algorithm on a 7 DoF Franka Emika Panda robot performing the learning of a pick&place task with continuous adaptation of the goal location and the via-points with human interactive corrections, triggered by our proposed approach. A validation study was conducted with 5 users in order to understand the reliability of the method., Workshop at ICRA 2022, May 22-27, 2022, Learning & Autonomous Control

This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment corresponding to each step in a task. The model is learned from human motion capture data in order to predict the human pose as realistically as possible. The second contribution is a combination of this prediction model with an informed graph search algorithm, which allows computation of human-robot cooperative plans with improved ergonomics according to the incorporated method for ergonomic assessment. The concepts have been evaluated in simulation and in a small user study in which the subjects manipulate a large object with a 32 DoF bimanual mobile robot as partner. For all subjects, the ergonomic-enhanced planner shows their reduced ergonomic cost compared to a baseline planner., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment corresponding to each step in a task. The model is learned from human motion capture data in order to predict the human pose as realistically as possible. The second contribution is a combination of this prediction model with an informed graph search algorithm, which allows computation of human-robot cooperative plans with improved ergonomics according to the incorporated method for ergonomic assessment. The concepts have been evaluated in simulation and in a small user study in which the subjects manipulate a large object with a 32 DoF bimanual mobile robot as partner. For all subjects, the ergonomic-enhanced planner shows their reduced ergonomic cost compared to a baseline planner., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

In electrically actuated robots most energy losses are due to the heating of the actuators. This energy loss can be greatly reduced with parallel elastic actuators, by optimizing the elastic element such that it delivers most of the required torques. Previously used optimization methods relied on parameterizing the spring characteristic, thereby limiting the set of spring characteristics optimized over and with that the loss reduction that can be obtained. This letter shows that such parametrization is not necessary; a method is presented to compute the optimal characteristic as an analytic function of the trajectory. The efficacy of this method is demonstrated using two examples. The first example considers the optimal spring characteristic for a parallel elastic actuator supporting the human ankle during walking. The second example applies the method in combination with trajectory optimization on a single degree of freedom robot performing a specific pick-and-place task. The task at hand has a height difference between the pick and the place location. With the analytical optimal spring, it is shown that the robot can recover enough of the energy released by the package to function without external electric energy supply., Accepted Author Manuscript, Learning & Autonomous Control, Robot Dynamics

In electrically actuated robots most energy losses are due to the heating of the actuators. This energy loss can be greatly reduced with parallel elastic actuators, by optimizing the elastic element such that it delivers most of the required torques. Previously used optimization methods relied on parameterizing the spring characteristic, thereby limiting the set of spring characteristics optimized over and with that the loss reduction that can be obtained. This letter shows that such parametrization is not necessary; a method is presented to compute the optimal characteristic as an analytic function of the trajectory. The efficacy of this method is demonstrated using two examples. The first example considers the optimal spring characteristic for a parallel elastic actuator supporting the human ankle during walking. The second example applies the method in combination with trajectory optimization on a single degree of freedom robot performing a specific pick-and-place task. The task at hand has a height difference between the pick and the place location. With the analytical optimal spring, it is shown that the robot can recover enough of the energy released by the package to function without external electric energy supply., Accepted Author Manuscript, Learning & Autonomous Control, Robot Dynamics