Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration. However, programming interactive and responsive behaviors for such systems is extremely challenging due to the nonlinearity and uncertainty inherent to their control. In this paper, we propose an approach for learning Bayesian Interaction Primitives for musculoskeletal robots given a limited set of example demonstrations. We show that this approach is capable of real-time state estimation and response generation for interaction with a robot for which no analytical model exists. Human-robot interaction experiments on a 'handshake' task show that the approach generalizes to new positions, interaction partners, and movement velocities.
Interactive Motion Planning for Human-Robot Collaboration Based on Human-Centric Configuration Space Ergonomic Field
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系，T-Stone机器人研究所，香港中文大学）；Idiap Research Institute（Idiap研究所）；Department of Advanced Robotics, Istituto Italiano di Tecnologia（先进机器人系，意大利Istituto Italiano di Tecnologia）
Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院）；Madeira Interactive Technology Institute, University of Madeira（马德拉大学马德拉交互技术研究所）
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e.g. stroke). During a physical therapy session, generating personalized feedback is critical to improve patient's engagement. However, prior work on socially assistive robotics for physical therapy has mainly utilized pre-defined corrective feedback even if patients have various physical and functional abilities. This paper presents an interactive approach of a socially assistive robot that can dynamically select kinematic feat…
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Interaction Primitives to enable predictive biomechanics: the prediction of future biomechanical states of a human partner conditioned on current observations and intended robot control signals. In turn, we leverage this capability within a model-predictive control strategy to identify the future ergonomic and biomechanical ramifications of potential rob…
We describe an ongoing project in learning to perform primitive actions from demonstrations using an interactive interface. In our previous work, we have used demonstrations captured from humans performing actions as training samples for a neural network-based trajectory model of actions to be performed by a computational agent in novel setups. We found that our original framework had some limitations that we hope to overcome by incorporating communication between the human and the computational agent, using the interaction between them to fine-tune the model learned by the machine. We propose…
University of Lincoln（林肯大学）；Centre for Autonomous Systems (L-CAS)（自主系统中心（L-CAS））
Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cases, the robot needs to safely push unripe fruits to reach a ripe one. Nonetheless, existing approaches to planning pushing movements in cluttered environments either are computationally expensive or only deal with 2-D cases and are not suitable for f…
University of Lincoln（林肯大学）；Centre for Autonomous Systems (L-CAS)（自主系统中心（L-CAS））
The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the l…
Tsinghua University（清华大学）；Institut de Rechecherche en Communications et Cybernétique de Nantes（南特通信与网络研究院）
During the conceptual and prototype design stage of an industrial product, it is crucial to take assembly/disassembly and maintenance operations in advance. A well-designed system should enable relatively easy access of operating manipulators in the constrained environment and reduce musculoskeletal disorder risks for those manual handling operations. Trajectory planning comes up as an important issue for those assembly and maintenance operations under a constrained environment, since it determines the accessibility and the other ergonomics issues, such as muscle effort and its related fatigue…
Interactive Expressive Motion Generation Using Dynamic Movement Primitives
Socially Intelligent Robotics Lab, Institute for Artificial Intelligence, University of Stuttgart（社会智能机器人实验室，人工智能研究所，斯图加特大学）；Collaborative Artificial Intelligence Group, Institute for Visualization and Interactive Systems, University of Stuttgart（协作人工智能小组，可视化与交互系统研究所，斯图加特大学）
The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for manufacturing applications. In this context, this paper proposes two hypotheses for ergonomically effective task delegation and HRC. The first hypothesis states that it is possible to quantify ergonomically professional tasks using motion data from a reduced set of sensors. Then, the most dangerous tasks can be delegated to a collaborative robot. The second hypothesis is that by including gesture recognition and spatial adaptation, the ergonomics…
College of Engineering, Peking University（北京大学工学院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are long-term and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized…
Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification. In the propo…
Chain Of Interaction Benchmark (COIN): When Reasoning meets Embodied Interaction
University of Science and Technology of China（中国科学技术大学）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；University of California, Los Angeles（加州大学洛杉矶分校）
University of Delaware（特拉华大学）；Princeton University（普林斯顿大学）
In this paper, we consider the problem of adapting a dynamically walking bipedal robot to follow a leading co-worker while engaging in tasks that require physical interaction. Our approach relies on switching among a family of Dynamic Movement Primitives (DMPs) as governed by a supervisor. We train the supervisor to orchestrate the switching among the DMPs in order to adapt to the leader's intentions, which are only implicitly available in the form of interaction forces. The primary contribution of our approach is its ability to furnish certificates of generalization to novel leader intentions…
CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation
Purdue University（普渡大学）；Indian Institute of Technology, Kharagpur（印度理工学院卡哈拉格普尔分校）；DEVCOM Army Research Lab（DEVCOM陆军研究实验室）；National University of Singapore（新加坡国立大学）
