Long-term planning for robots operating in domestic environments poses unique challenges due to the interactions between humans, objects, and spaces. Recent advancements in trajectory planning have leveraged vision-language models (VLMs) to extract contextual information for robots operating in real-world environments. While these methods achieve satisfying performance, they do not explicitly model human activities. Such activities influence surrounding objects and reshape spatial constraints. This paper presents a novel approach to trajectory planning that integrates human preferences, activities, and spatial context through an enriched 3D scene graph (3DSG) representation. By incorporating activity-based relationships, our method captures the spatial impact of human actions, leading to more context-sensitive trajectory adaptation. Preliminary results demonstrate that our approach effectively assigns costs to spaces influenced by human activities, ensuring that the robot trajectory remains contextually appropriate and sensitive to the ongoing environment. This balance between task efficiency and social appropriateness enhances context-aware human-robot interactions in domestic settings. Future work includes implementing a full planning pipeline and conducting user studies to evaluate trajectory acceptability.

Aligning robot navigation with human preferences is essential for ensuring comfortable and predictable robot movement in shared spaces, facilitating seamless human-robot coexistence. While preference-based learning methods, such as reinforcement learning from human feedback (RLHF), enable this alignment, the choice of the preference collection interface may influence the process. Traditional 2D interfaces provide structured views but lack spatial depth, whereas immersive VR offers richer perception, potentially affecting preference articulation. This study systematically examines how the interface modality impacts human preference collection and navigation policy alignment. We introduce a novel dataset of 2,325 human preference queries collected through both VR and 2D interfaces, revealing significant differences in user experience, preference consistency, and policy outcomes. Our findings highlight the trade-offs between immersion, perception, and preference reliability, emphasizing the importance of interface selection in preference-based robot learning. The dataset will be publicly released to support future research.

Modern embodied artificial agents excel in static, predefined tasks but fall short in dynamic and long-term interactions with humans. On the other hand, humans can adapt and evolve continuously, exploiting the situated knowledge embedded in their environment and other agents, thus contributing to meaningful interactions. We introduce the concept of co-existence for embodied artificial agents and argues that it is a prerequisite for meaningful, long-term interaction with humans. We take inspiration from biology and design theory to understand how human and non-human organisms foster entities that co-exist within their specific niches. Finally, we propose key research directions for the machine learning community to foster co-existing embodied agents, focusing on the principles, hardware and learning methods responsible for shaping them.

When encountering a robot in the wild, it is not inherently clear to human users what the robot's capabilities are. When encountering misunderstandings or problems in spoken interaction, robots often just apologize and move on, without additional effort to make sure the user understands what happened. We set out to compare the effect of two speech based capability communication strategies (proactive, reactive) to a robot without such a strategy, in regard to the user's rating of and their behavior during the interaction. For this, we conducted an in-person user study with 120 participants who had three speech-based interactions with a social robot in a restaurant setting. Our results suggest that users preferred the robot communicating its capabilities proactively and adjusted their behavior in those interactions, using a more conversational interaction style while also enjoying the interaction more.

To facilitate human-robot interaction and gain human trust, a robot should recognize and adapt to changes in human behavior. This work documents different human behaviors observed while taking objects from an interactive robot in an experimental study, categorized across two dimensions: pull force applied and handedness. We also present the changes observed in human behavior upon repeated interaction with the robot to take various objects.

In most applications, robots need to adapt to new environments and be multi-functional without forgetting previous information. This requirement gains further importance in real-world scenarios where robots operate in coexistence with humans. In these complex environments, human actions inevitably lead to changes, requiring robots to adapt accordingly. To effectively address these dynamics, the concept of continual learning proves essential. It not only enables learning models to integrate new knowledge while preserving existing information but also facilitates the acquisition of insights from diverse contexts. This aspect is particularly relevant to the issue of context-switching, where robots must navigate and adapt to changing situational dynamics. Our approach introduces a novel approach to effectively tackle the problem of context drifts by designing a Streaming Graph Neural Network that incorporates both regularization and rehearsal techniques. Our Continual\_GTM model enables us to retain previous knowledge from different contexts, and it is more effective than traditional fine-tuning approaches. We evaluated the efficacy of Continual\_GTM in predicting human routines within household environments, leveraging spatio-temporal object dynamics across diverse scenarios.

Ambiguities are common in human-robot interaction, especially when a robot follows user instructions in a large collocated space. For instance, when the user asks the robot to find an object in a home environment, the object might be in several places depending on its varying semantic properties (e.g., a bowl can be in the kitchen cabinet or on the dining room table, depending on whether it is clean/dirty, full/empty and the other objects around it). Previous works on object semantics have predicted such relationships using one shot-inferences which are likely to fail for ambiguous or partially understood instructions. This paper focuses on this gap and suggests a semantically-driven disambiguation approach by utilizing follow-up clarifications to handle such uncertainties. To achieve this, we first obtain semantic knowledge embeddings, and then these embeddings are used to generate clarifying questions by following an iterative process. The evaluation of our method shows that our approach is model agnostic, i.e., applicable to different semantic embedding models, and follow-up clarifications improve the performance regardless of the embedding model. Additionally, our ablation studies show the significance of informative clarifications and iterative predictions to enhance system accuracies.

Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approach the sample-efficiency challenge by expanding the information collected per query to contain both preferences and optional text prompting. To accomplish this, we leverage the zero-shot capabilities of a large language model (LLM) to reason from the text provided by humans. To accommodate the additional query information, we reformulate the reward learning objectives to contain flexible highlights -- state-action pairs that contain relatively high information and are related to the features processed in a zero-shot fashion from a pretrained LLM. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications. Additionally, the collective feedback collected serves to train a robot on socially compliant trajectories in a simulated social navigation landscape. We provide video examples of the trained policies at https://sites.google.com/view/rl-predilect

Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot's explanation level at a certain round but also the prior information they received from the robot.

Although initially emerging in psychology, many of the concerns raised, such as lack of open access to data, materials, and/or experimental design apply also to other (social) sciences. Among these are both Human Robot Interaction (HRI) and its related sub-field of Child Robot Interaction (CRI), where social and psychological relationships between humans and robots are often the focus of the research. Given its novelty and rapidly evolving progress, CRI in particular suffers from fragmented and heterogeneous literature, varying research goals, and a lack of standardised methods and metrics. Recent efforts have brought forth conversations related to replication specifically within CRI [51, 52], with authors appealing for more works that address the main challenges in HRI with children whilst still ensuring high-quality reporting and data sharing. However, clear open science guidelines on reproducibility in HRI and related sub-fields are still missing.