Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-time pose estimation, the practical value of many existing approaches is limited by the lack of future predictions and consideration of physical constraints. Conventional motion prediction schemes rely heavily on past poses, which are not always available in real-world scenarios. To address these limitations, we present a physics-informed learning framework that integrates domain knowledge into both training and inference to predict human motion using inertial measurements from only 5 IMUs. We propose a network that accounts for the spatial characteristics of human movements. During training, we incorporate forward and differential kinematics functions as additional loss components to regularize the learned joint predictions. At the inference stage, we refine the prediction from the previous iteration to update a joint state buffer, which is used as extra inputs to the network. Experimental results demonstrate that our approach achieves high accuracy, smooth transitions between motions, and generalizes well to unseen subjects. The source code and data are available at https://github.com/ami–iit/paper_guo_2025_iros_human_kinematics_prediction.

In this paper, we propose a motion, stiffness, and force learning framework based on an extended dynamic movement primitive (DMP) and quadratic programming (QP) optimization. The objective is to learn kinematic and dynamic operational parameters from a one-shot human demonstration, through measurement and estimation of the motion, 3-dimensional (3-D) endpoint stiffness, and applied forces of the human arm during manipulation tasks. To this end, first, the framework features an extended DMP to model the motion, stiffness, and force variations in Cartesian space and 2-D sphere manifold. Second, to account for collected errors and human-robot operation gaps, a QP optimization is applied to fine-tune the desired position of the controller. Finally, we validate the framework through two experiments in real scenarios on the Franka Emika Panda robot. Experimental results show that the robot can not only inherit the variation laws of motion, stiffness, and force in the human demonstration, but also exhibit certain generalization capabilities to other situations. The framework provides a reference for robots learning multiple skills via a one-shot human demonstration, which finds great potential application in human-robot cooperation, contact-rich scenarios, and skillful operations, where the motion, stiffness, and applied forces need to be considered simultaneously. Note to Practitioners—Fast programming in robotics through skill transfer plays a critical role in next-generation robots entering ordinary people’s lives. Existing research focuses more on skill learning at the kinematic level and lacks on the dynamic level, such as stiffness and contact force. The goal of this paper is to propose a novel framework for robots learning of motion, stiffness, and force variations from a one-shot human demonstration, simultaneously. To this end, a Riemannian-based DMP method is employed to model the variation laws of motion, stiffness, and force in Cartesian space and 2-D sphere manifold, respectively. In this way, the learning module needs to be run only once, and the patterns can also be generalized to other targets without repeated robot teaching and additional time-consuming processes. To accurately reproduce the learned skills, a human-like motion/stiffness/force controller combined with QP optimization is investigated. In this paper, rather than identifying real environmental parameters, we directly use interacted forces during the human demonstration to represent environmental effects and employ QP to update the desired position in a limited range to account for collected errors and human-robot operation gaps. Experiments on button pressing and polishing tasks by the Panda robot have achieved very good results. The work of this paper lays a foundation for multiple skills learning from human demonstration (LfHD).

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

Preserving and encouraging mobility in the elderly and adults with chronic conditions is of paramount importance. However, existing walking aids are either inadequate to provide sufficient support to users’ stability or too bulky and poorly maneuverable to be used outside hospital environments. In addition, they all lack adaptability to individual requirements. To address these challenges, this paper introduces WANDER, a novel Walking Assistive omNi-Directional Exo-Robot. It consists of an omnidirectional platform and a robust aluminum structure mounted on top of it, which provides partial body weight support. A comfortable and minimally restrictive coupling interface embedded with a force/torque sensor allows to detect users’ intentions, which are translated into command velocities by means of a variable admittance controller. An optimization technique based on users’ preferences, i.e., Preference-Based Optimization (PBO) guides the choice of the admittance parameters (i.e., virtual mass and damping) to better fit subject-specific needs and characteristics. Experiments with twelve healthy subjects exhibited a significant decrease in energy consumption and jerk when using WANDER with PBO parameters as well as improved user performance and comfort. The great interpersonal variability in the optimized parameters highlights the importance of personalized control settings when walking with an assistive device, aiming to enhance users’ comfort and mobility while ensuring reliable physical support.

Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.

In this paper, a novel human-robot collaborative framework for mixed case palletizing is presented. The framework addresses several challenges associated with the detection and localisation of boxes and pallets through visual perception algorithms, high-level optimisation of the collaborative effort through effective role-allocation principles, and maximisation of packing density. A graphical user interface (GUI) is additionally developed to ensure an intuitive allocation of roles and the optimal placement of the boxes on target pallets. The framework is evaluated in two conditions where humans operate with and without the support of a Mobile COllaborative robotic Assistant (MOCA). The results show that the optimised placement can improve up to the 20% with respect to a manual execution of the same task, and reveal the high potential of MOCA in increasing the performance of collaborative palletizing tasks.