Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most robotics dressing strategies considered a single robot only, which greatly limited the performance of the dressing assistance. In fact, healthcare professionals perform the task bimanually. Inspired by them, we propose a bimanual cooperative scheme for robotic dressing assistance. In the scheme, an interactive robot joins hands with the human thus supporting/guiding the human in the dressing process while the dressing robot performs the dressing task. We identify a key feature: the elbow angle that affects the dressing action and propose an optimal strategy for the interactive robot using the feature. A dressing coordinate based on the posture of the arm is defined to better encode the dressing policy. We validate the interactive dressing scheme with extensive experiments and also an ablation study.

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The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.

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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.@en

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations.@en

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.

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Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demonstrations are decomposed into a set of movement primitives by inferring the underlying sequential structure of the task. The decomposition is based on a novel probability distribution which we call Directional Normal Distribution. The distribution allows infering the movement primitive's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstrations. In addition, it permits determining an appropriate number of movement primitives for a task via model selection. After finding the task's composition, the system learns to sequence the resulting movement primitives in order to be able to reproduce the task on a real robot. We evaluate the approach on three different tasks, unscrewing a light bulb, box stacking and box flipping. All tasks are kinesthetically demonstrated and then reproduced on a Barrett WAM robot.

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In this letter, we introduce Mixture of Attractors, a novel movement primitive representation that allows for learning complex object-relative movements. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen object positions and orientations. In contrast to most other approaches, a skill is learned by solving a convex optimization problem. Therefore, the quality of the skill does not depend on a good initial estimate of parameters. The resulting movements are automatically smooth and can be of arbitrary shape. The approach is evaluated and compared to other movement primitive representations on data from the Omniglot handwriting dataset and on real demonstrations of a handwriting task. The evaluations show that the presented approach outperforms other state-of-the-art concepts in terms of generalization capabilities and accuracy.

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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 cooperation including contact changes, and a novel architecture combining force interaction cues, interactive search-based planning, and online trajectory and motion generation. The resulting system implements a mixed initiative collaboration strategy, deferring to the human when his intentions are unclear, and driving the task once understood. This results in an easy and intuitive human-robot interaction. It is evaluated in simulations and on a bi-manual mobile robot with 32 degrees of freedom.

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Virtual avatars have been employed in many contexts, from simple conversational agents to communicating the internal state and intentions of large robots when interacting with humans. Rarely, however, are they employed in scenarios which require non-verbal communication of spatial information or dynamic interaction from a variety of perspectives. When presented on a flat screen, many illusions and visual artifacts interfere with such applications, which leads to a strong preference for physically-actuated heads and faces. By adjusting the perspective projection used to render 3D avatars to match a viewer's physical perspective, they could provide a useful middle ground between typical 2D/3D avatar representations, which are often ambiguous in their spatial relationships, and physically-actuated heads/faces, which can be difficult to construct or impractical to use in some environments. A user study was conducted to determine to what extent a head-tracked perspective projection scheme was able to mitigate the issues in readability of a 3D avatar's expression or gaze target compared to use of a standard perspective projection. To the authors' knowledge, this is the first user study to perform such a comparison, and the results show not only an overall improvement in viewers' accuracy when attempting to follow the avatar's gaze, but a reduction in spatial biases in predictions made from oblique viewing angles@en

Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a probability distribution we call Directional Normal Distribution (DND). To capture the sequential properties of the manipulation task, we model the demonstrations with a Hidden Markov Model (HMM). Here, we employ mixtures of DNDs as the HMM's output emissions. The combination of HMMs and mixtures of DNDs allows to infer the MP's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstration data. In addition, it permits to determine an appropriate number of MPs that explains the demonstrations best. We evaluate the approach on kinesthetic demonstrations of a light bulb unscrewing task. Decomposing the task leads to intuitive and meaningful MPs that reflect the natural structure of the task.

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