ReLI: A Language-Agnostic Approach to Human-Robot Interaction

Adapting autonomous agents to industrial, domestic, and other daily tasks is currently gaining momentum. However, in the global or cross-lingual application contexts, ensuring effective interaction with the environment and executing unrestricted human task-specified instructions in diverse languages remains an unsolved problem. To address this challenge, we propose ReLI, a language-agnostic framework designed to enable autonomous agents to converse naturally, semantically reason about the environment, and to perform downstream tasks, regardless of the task instruction's linguistic origin. First, we ground large-scale pre-trained foundation models and transform them into language-to-action models that can directly provide common-sense reasoning and high-level robot control through natural, free-flow human-robot conversational interactions. Further, we perform cross-lingual grounding of the models to ensure that ReLI generalises across the global languages. To demonstrate the ReLI's robustness, we conducted extensive simulated and real-world experiments on various short- and long-horizon tasks, including zero-shot and few-shot spatial navigation, scene information retrieval, and query-oriented tasks. We benchmarked the performance on 140 languages involving over 70K multi-turn conversations. On average, ReLI achieved over 90%$\pm$0.2 accuracy in cross-lingual instruction parsing and task execution success rates. These results demonstrate the ReLI's potential to enhance natural human-robot interaction in the real world while championing linguistic diversity. Demonstrations and resources will be publicly available at https://linusnep.github.io/ReLI/.

EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments

To ensure the efficiency of robot autonomy under diverse real-world conditions, a high-quality heterogeneous dataset is essential to benchmark the operating algorithms' performance and robustness. Current benchmarks predominantly focus on urban terrains, specifically for on-road autonomous driving, leaving multi-degraded, densely vegetated, dynamic and feature-sparse environments, such as underground tunnels, natural fields, and modern indoor spaces underrepresented. To fill this gap, we introduce EnvoDat, a large-scale, multi-modal dataset collected in diverse environments and conditions, including high illumination, fog, rain, and zero visibility at different times of the day. Overall, EnvoDat contains 26 sequences from 13 scenes, 10 sensing modalities, over 1.9TB of data, and over 89K fine-grained polygon-based annotations for more than 82 object and terrain classes. We post-processed EnvoDat in different formats that support benchmarking SLAM and supervised learning algorithms, and fine-tuning multimodal vision models. With EnvoDat, we contribute to environment-resilient robotic autonomy in areas where the conditions are extremely challenging. The datasets and other relevant resources can be accessed through https://linusnep.github.io/EnvoDat/.