NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation

Robotic platforms that can efficiently collaborate with humans in physical tasks constitute a major goal in robotics. However, many existing robotic platforms are either designed for social interaction or industrial object manipulation tasks. The design of collaborative robots seldom emphasizes both their social interaction and physical collaboration abilities. To bridge this gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator. NICOL is a large, newly designed, scaled-up version of its well-evaluated predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and facial expression display and extends its manipulation abilities in terms of precision, object size, and workspace size. Our contribution in this paper is twofold -- firstly, we introduce the design concept for NICOL, and secondly, we provide an evaluation of NICOL's manipulation abilities by presenting a novel extension for an end-to-end hybrid neuro-genetic visuomotor learning approach adapted to NICOL's more complex kinematics. We show that the approach outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK and BIO-IK. Overall, this article presents for the first time the humanoid robot NICOL, and contributes to the integration of social robotics and neural visuomotor learning for humanoid robots

Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

Dyadic behavior in co-manipulation :from humans to robots

To both decrease the physical toll on a human worker, and increase a robot’s environment perception, a human-robot dyad may be used to co-manipulate a shared object. From the premise that humans are efficient working together, this work’s approach is to investigate human-human dyads co-manipulating an object. The co-manipulation is evaluated from motion capture data, surface electromyography (EMG) sensors, and custom contact sensors for qualitative performance analysis. A human-human dyadic co-manipulation experiment is designed in which every human is instructed to behave as a leader, as a follower or neither, acting as naturally as possible. The experiment data analysis revealed that humans modulate their arm mechanical impedance depending on their role during the co-manipulation. In order to emulate the human behavior during a co-manipulation task, an admittance controller with varying stiffness is presented. The desired stiffness is continuously varied based on a scalar and smooth function that assigns a degree of leadership to the robot. Furthermore, the controller is analyzed through simulations, its stability is analyzed by Lyapunov. The resulting object trajectories greatly resemble the patterns seen in the human-human dyad experiment.Para tanto diminuir o esforço físico de um humano, quanto aumentar a percepção de um ambiente por um robô, um díade humano-robô pode ser usado para co-manipulação de um objeto compartilhado. Partindo da premissa de que humanos são eficientes trabalhando juntos, a abordagem deste trabalho é a de investigar díades humano-humano co-manipulando um objeto compartilhado. A co-manipulação é avaliada a partir de dados de um sistema de captura de movimentos, sinais de eletromiografia (EMG), e de sensores de contato customizados para análise qualitativa de desempenho. Um experimento de co-manipulação com díades humano-humano foi projetado no qual cada humano é instruído a se comportar como um líder, um seguidor, ou simplesmente agir tão naturalmente quanto possível. A análise de dados do experimento revelou que os humanos modulam a rigidez mecânica do braço a depender de que tipo de comportamento eles foram designados antes da co-manipulação. Para emular o comportamento humano durante uma tarefa de co-manipulação, um controle por admitância com rigidez variável é apresentado neste trabalho. A rigidez desejada é continuamente variada com base em uma função escalar suave que define o grau de liderança do robô. Além disso, o controlador é analisado por meio de simulações, e sua estabilidade é analisada pela teoria de Lyapunov. As trajetórias resultantes do uso do controlador mostraram um padrão de comportamento muito parecido ao do experimento com díades humano-humano

Predictive and Robust Robot Assistance for Sequential Manipulation

This paper presents a novel concept to support physically impaired humans in daily object manipulation tasks with a robot. Given a user's manipulation sequence, we propose a predictive model that uniquely casts the user's sequential behavior as well as a robot support intervention into a hierarchical multi-objective optimization problem. A major contribution is the prediction formulation, which allows to consider several different future paths concurrently. The second contribution is the encoding of a general notion of constancy constraints, which allows to consider dependencies between consecutive or far apart keyframes (in time or space) of a sequential task. We perform numerical studies, simulations and robot experiments to analyse and evaluate the proposed method in several table top tasks where a robot supports impaired users by predicting their posture and proactively re-arranging objects

Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. Virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. We present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (Original Policies) solely in simulation for four robotic caregiving tasks (robot-assisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed Revised Policies based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly disagreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.Comment: IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 8 pages, 8 figures, 2 table

Perception and manipulation for robot-assisted dressing

Assistive robots have the potential to provide tremendous support for disabled and elderly people in their daily dressing activities. This thesis presents a series of perception and manipulation algorithms for robot-assisted dressing, including: garment perception and grasping prior to robot-assisted dressing, real-time user posture tracking during robot-assisted dressing for (simulated) impaired users with limited upper-body movement capability, and finally a pipeline for robot-assisted dressing for (simulated) paralyzed users who have lost the ability to move their limbs. First, the thesis explores learning suitable grasping points on a garment prior to robot-assisted dressing. Robots should be endowed with the ability to autonomously recognize the garment state, grasp and hand the garment to the user and subsequently complete the dressing process. This is addressed by introducing a supervised deep neural network to locate grasping points. To reduce the amount of real data required, which is costly to collect, the power of simulation is leveraged to produce large amounts of labeled data. Unexpected user movements should be taken into account during dressing when planning robot dressing trajectories. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. A probabilistic real-time tracking method is proposed using Bayesian networks in latent spaces, which fuses multi-modal sensor information. The latent spaces are created before dressing by modeling the user movements, taking the user's movement limitations and preferences into account. The tracking method is then combined with hierarchical multi-task control to minimize the force between the user and the robot. The proposed method enables the Baxter robot to provide personalized dressing assistance for users with (simulated) upper-body impairments. Finally, a pipeline for dressing (simulated) paralyzed patients using a mobile dual-armed robot is presented. The robot grasps a hospital gown naturally hung on a rail, and moves around the bed to finish the upper-body dressing of a hospital training manikin. To further improve simulations for garment grasping, this thesis proposes to update more realistic physical properties values for the simulated garment. This is achieved by measuring physical similarity in the latent space using contrastive loss, which maps physically similar examples to nearby points.Open Acces