As robots enter human workspaces, there is a crucial need for them to comprehend embodied human instructions, enabling intuitive and fluent human-robot interaction (HRI). However, accurate comprehension is challenging due to a lack of large-scale datasets that capture natural embodied interactions in diverse HRI settings. Existing datasets suffer from perspective bias, single-view data collection, inadequate coverage of nonverbal gestures, and a predominant focus on indoor environments. To address these issues, we present the Refer360 dataset, a large-scale dataset of embodied verbal and nonverbal interactions collected across diverse viewpoints in both indoor and outdoor settings. Additionally, we introduce MuRes, a multimodal guided residual module designed to improve embodied referring expression comprehension. MuRes acts as an information bottleneck, extracting salient modality-specific signals and reinforcing them into pre-trained representations to form complementary features for downstream tasks. We conduct extensive experiments on four datasets, including our Refer360 dataset, and demonstrate that current multimodal models fail to capture embodied interactions comprehensively; however, augmenting them with MuRes consistently improves performance. These findings establish Refer360 as a valuable benchmark and exhibit the potential of guided residual learning to advance embodied referring expression comprehension in robots operating within human environments.

Humans predominantly use verbal utterances and nonverbal gestures (e.g., eye gaze and pointing gestures) in their natural interactions. For instance, pointing gestures and verbal information is often required to comprehend questions such as "what object is that?" Thus, this question-answering (QA) task involves complex reasoning of multimodal expressions (verbal utterances and nonverbal gestures). However, prior works have explored QA tasks in non-embodied settings, where questions solely contain verbal utterances from a single verbal and visual perspective. In this paper, we have introduced 8 novel embodied question answering (EQA) tasks to develop learning models to comprehend embodied questions with multimodal expressions. We have developed a novel large-scale dataset, EQA-MX, with over 8 million diverse embodied QA data samples involving multimodal expressions from multiple visual and verbal perspectives. To learn salient multimodal representations from discrete verbal embeddings and continuous wrapping of multiview visual representations, we propose a vector-quantization (VQ) based multimodal representation learning model, VQ-Fusion, for the EQA tasks. Our extensive experimental results suggest that VQ-Fusion can improve the performance of existing state-of-the-art visual-language models up to 13% across EQA tasks.

Humans naturally use referring expressions with verbal utterances and nonverbal gestures to refer to objects and events. As these referring expressions can be interpreted differently from the speaker's or the observer's perspective, people effectively decide on the perspective in comprehending the expressions. However, existing models do not explicitly learn perspective grounding, which often causes the models to perform poorly in understanding embodied referring expressions. To make it exacerbate, these models are often trained on datasets collected in non-embodied settings without nonverbal gestures and curated from an exocentric perspective. To address these issues, in this paper, we present a perspective-aware multitask learning model, called PATRON, for relation and object grounding tasks in embodied settings by utilizing verbal utterances and nonverbal cues. In PATRON, we have developed a guided fusion approach, where a perspective grounding task guides the relation and object grounding task. Through this approach, PATRON learns disentangled task-specific and task-guidance representations, where task-guidance representations guide the extraction of salient multimodal features to ground the relation and object accurately. Furthermore, we have curated a synthetic dataset of embodied referring expressions with multimodal cues, called CAESAR-PRO. The experimental results suggest that PATRON outperforms the evaluated state-of-the-art visual-language models. Additionally, the results indicate that learning to ground perspective helps machine learning models to improve the performance of the relation and object grounding task. Furthermore, the insights from the extensive experimental results and the proposed dataset will enable researchers to evaluate visual-language models' effectiveness in understanding referring expressions in other embodied settings.

Humans naturally use verbal utterances and nonverbal gestures to refer to various objects (known as $\textit{referring expressions}$) in different interactional scenarios. As collecting real human interaction datasets are costly and laborious, synthetic datasets are often used to train models to unambiguously detect relationships among objects. However, existing synthetic data generation tools that provide referring expressions generally neglect nonverbal gestures. Additionally, while a few small-scale datasets contain multimodal cues (verbal and nonverbal), these datasets only capture the nonverbal gestures from an exo-centric perspective (observer). As models can use complementary information from multimodal cues to recognize referring expressions, generating multimodal data from multiple views can help to develop robust models. To address these critical issues, in this paper, we present a novel embodied simulator, CAESAR, to generate multimodal referring expressions containing both verbal utterances and nonverbal cues captured from multiple views. Using our simulator, we have generated two large-scale embodied referring expression datasets, which we have released publicly. We have conducted experimental analyses on embodied spatial relation grounding using various state-of-the-art baseline models. Our experimental results suggest that visual perspective affects the models' performance; and that nonverbal cues improve spatial relation grounding accuracy. Finally, we will release the simulator publicly to allow researchers to generate new embodied interaction datasets.