Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.

This study evaluates the performance and usability of Mixed Reality (MR), Virtual Reality (VR), and camera stream interfaces for remote error resolution tasks, such as correcting warehouse packaging errors. Specifically, we consider a scenario where a robotic arm halts after detecting an error, requiring a remote operator to intervene and resolve it via pick-and-place actions. Twenty-one participants performed simulated pick-and - place tasks using each interface. A linear mixed model (LMM) analysis of task resolution time, usability scores (SUS), and mental workload scores (NASA- TLX) showed that the MR interface outperformed both VR and camera interfaces. MR enabled significantly faster task completion, was rated higher in usability, and was perceived to be less cognitively demanding. Notably, the MR interface, which projected a virtual robot onto a physical table, provided superior spatial understanding and physical reference cues. Post-study surveys further confirmed participants' preference for MR over other interfaces.

The first workshop on human-robot interaction in extreme and challenging environments (exactingHRI: https://sites.google.com/monash.edu/exactinghril) focuses on the forefront of HRI research in applications where robots are working with diverse users in uncertain, unknown, or risky environments to deliver reliable outcomes in repeated sessions. In these scenarios, a robot's autonomous and interactive functions are put to the test, with errors likely to arise. Such HRI systems require design and evaluation in-situ with target users, i.e., “exacting” HRI. The exactingHRI 2025 workshop aims to bring together researchers that investigate the diverse human, robot, task, environment, and interaction factors that are challenging for state-of-the-art HRI systems, as well as innovative designs, theories, models, and methods that equip people and robots with the ability to address these challenges. Workshop presenters will share lessons they have learned from successful or failed attempts in testing their work in such difficult settings, in a bid to encourage and guide the necessary efforts that progress our field to solve real-world problems.

Navigating effectively and safely in unknown urban environments is a crucial ability for service robot applications such as last-mile package delivery. To reach the entrance of its target destination, the robot must make informed local and global path planning decisions. We present a mapless global planning strategy based on pedestrian following. Our method allows the robot to exploit natural routes taken by surrounding pedestrians to make informed and efficient path planning decisions for reaching its goal. The algorithm also includes a recovery system to assist the robot when insufficient progress is made (i.e. robot stuck in dead end). Once the robot is within the vicinity of the target building, a wall following behaviour is used to reach the entrance of the target building. Simulated experiments and a proof-of-concept demonstration on a real robot were shown to validate the approach.

When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from nonexpert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot would also be able to infer a human's true objectives even when the human fails to properly demonstrate these objectives due to a lack of expertise. In this article, we propose to jointly infer the expertise level and the objective function of a human given observations of their (possibly) nonoptimal demonstrations. Two inference approaches are proposed. In the first approach, inference is done over a finite discrete set of possible objective functions and expertise levels. In the second approach, the robot optimizes over the space of all possible hypotheses and finds the objective function and the expertise level that best explain the observed human behavior. We demonstrate our proposed approaches both in simulation and with real user data.

Research in biomechanics hypothesizes that human motion is optimal with respect to an unknown cost function that varies depending on the action and/or task. This unknown cost function is often approximated as the weighted sum of a set of features or basis cost functions. As a person performs a sequence of actions, the weights associated to each of these basis functions are likely to vary over time. Given a human demonstration and the corresponding cost weight trajectory recovered via inverse optimal control (IOC), this paper proposes an optimal control (OC) method that can generate robot motion based on human movement using time-varying cost function weights. By using time-varying weights, the proposed optimal control method can handle changing optimization criteria without segmentation. The method is evaluated both in simulation and with recorded human data. Using human demonstration data, we demonstrate the reproduction of pick-and-place motions with an average end-effector error at the pick place location within 0.82 cm, which is significantly lower than the average trajectory error, indicating that the approach correctly prioritizes reaching the pick and place locations without manual segmentation.

This paper proposes a human-aware motion planner building on RRT-Connect, dubbed Human-Aware RRT-Connect. The planner considers a composite cost function that includes four criteria: human separation distance, human-robot center of mass distance, robot inertia and visibility. This choice of criteria ensures the robot maintains a safe distance and low inertia during motion while being as visible as possible to the human. A simulation study is conducted to demonstrate the planner performance. For the simulation study, the proposed offline Human-Aware RRT-Connect planner is compared to other offline planners through a set of scenarios that vary in environment and task complexity. Several human-robot configurations are tested in a shared workspace involving a simulated Franka Emika Panda arm and a human model. The paths generated by the Human-Aware RRT-Connect planner maintain larger separation distances from the human, are of lower inertia, and are more visible.

Human-Robot Interaction (HRI) user studies are challenging to evaluate and compare due to a lack of standardization and the infrastructure required to implement each study. The lack of experimental infrastructure also makes it difficult to systematically evaluate the impact of individual components (e.g., the quality of perception software) on overall system performance. This work proposes a framework to ease the implementation and reproducibility of human-robot interaction user studies. The framework utilizes ROS middleware and is implemented with four modules: perception, decision, action, and metrics. The perception module aggregates sensor data to be used by the decision and action modules. The decision module is the task-level executive and can be designed by the HRI researcher for their specific task. The action module takes subtask requests from the decision module and breaks them down into motion primitives for execution on the robot. The metrics module tracks and generates quantitative metrics for the study. The framework is implemented with modular interfaces to allow for alternate implementations within each module and can be generalized for a variety of tasks and human/robot roles. The framework is illustrated through an example scenario involving a human and a Franka Emika Panda arm collaboratively assembling a toolbox together.

Estimating a user's expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it's interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user's expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the “goodness” of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.

Endowing animated virtual characters with emotionally expressive behaviors is paramount to improving the quality of the interactions between humans and virtual characters. Full-body motion, in particular, with its subtle kinematic variations, represents an effective way of conveying emotionally expressive content. However, before synthesizing expressive full-body movements, it is necessary to identify and understand what qualities of human motion are salient to the perception of emotions and how these qualities can be exploited to generate novel and equally expressive full-body movements. Based on previous studies, we argue that it is possible to perceive and generate expressive full-body movements from a limited set of joint trajectories, including end-effector trajectories and additional constraints such as pelvis and elbow trajectories. Hence, these selected trajectories define a significant and reduced motion space, which is adequate for the characterization of the expressive qualities of human motion and that is both suitable for the analysis and generation of emotionally expressive full-body movements. The purpose and main contribution of this work is the methodological framework we defined and used to assess the validity and applicability of the selected trajectories for the perception and generation of expressive full-body movements. This framework consists of the creation of a motion capture database of expressive theatrical movements, the development of a motion synthesis system based on trajectories re-played or re-sampled and inverse kinematics, and two perceptual studies.

Recent results in the affective computing sciences point towards the importance of virtual characters capable of conveying affect through their movements. However, in spite of all advances made on the synthesis of expressive motions, almost all of the existing approaches focus on the translation of stylistic content rather than on the generation of new expressive motions. Based on studies that show the importance of end-effector trajectories in the perception and recognition of affect, this paper proposes a new approach for the automatic generation of affective motions. In this approach, expressive content is embedded in a low-dimensional manifold built from the observation of end-effector trajectories. These trajectories are taken from an expressive motion capture database. Body motions are then reconstructed by a multi-chain Inverse Kinematics controller. The similarity between the expressive content of MoCap and synthesized motions is quantitatively assessed through information theory measures.

Virtual characters capable of showing emotional content are considered as more believable and engaging. However, in spite of the numerous psychological studies and machine learning applications trying to decode the most salient features in the expression and perception of affect, there is still no common understanding about how affect is conveyed through body motions. Based on findings reported by the psychology research community and quantitative results obtained in the computer animation domain during the last years, we propose to represent affective bodily movement through a low-dimensional parameterization consisting of the spatio-temporal trajectories of eight main joints in the human body (hands, head, feet, elbows and pelvis). Using a combined evaluation protocol, we show that this low-dimensional parameterization and the features derived from it are a compact and sufficient representation of affective motions that can be used for automatic recognition of affect and the generation of new affective-expressive motions.