SpatialPrompting: Keyframe-driven Zero-Shot Spatial Reasoning with Off-the-Shelf Multimodal Large Language Models

This study introduces SpatialPrompting, a novel framework that harnesses the emergent reasoning capabilities of off-the-shelf multimodal large language models to achieve zero-shot spatial reasoning in three-dimensional (3D) environments. Unlike existing methods that rely on expensive 3D-specific fine-tuning with specialized 3D inputs such as point clouds or voxel-based features, SpatialPrompting employs a keyframe-driven prompt generation strategy. This framework uses metrics such as vision-language similarity, Mahalanobis distance, field of view, and image sharpness to select a diverse and informative set of keyframes from image sequences and then integrates them with corresponding camera pose data to effectively abstract spatial relationships and infer complex 3D structures. The proposed framework not only establishes a new paradigm for flexible spatial reasoning that utilizes intuitive visual and positional cues but also achieves state-of-the-art zero-shot performance on benchmark datasets, such as ScanQA and SQA3D, across several metrics. The proposed method effectively eliminates the need for specialized 3D inputs and fine-tuning, offering a simpler and more scalable alternative to conventional approaches.

Online Embedding Multi-Scale CLIP Features into 3D Maps

This study introduces a novel approach to online embedding of multi-scale CLIP (Contrastive Language-Image Pre-Training) features into 3D maps. By harnessing CLIP, this methodology surpasses the constraints of conventional vocabulary-limited methods and enables the incorporation of semantic information into the resultant maps. While recent approaches have explored the embedding of multi-modal features in maps, they often impose significant computational costs, lacking practicality for exploring unfamiliar environments in real time. Our approach tackles these challenges by efficiently computing and embedding multi-scale CLIP features, thereby facilitating the exploration of unfamiliar environments through real-time map generation. Moreover, the embedding CLIP features into the resultant maps makes offline retrieval via linguistic queries feasible. In essence, our approach simultaneously achieves real-time object search and mapping of unfamiliar environments. Additionally, we propose a zero-shot object-goal navigation system based on our mapping approach, and we validate its efficacy through object-goal navigation, offline object retrieval, and multi-object-goal navigation in both simulated environments and real robot experiments. The findings demonstrate that our method not only exhibits swifter performance than state-of-the-art mapping methods but also surpasses them in terms of the success rate of object-goal navigation tasks.

Language to Map: Topological map generation from natural language path instructions

In this paper, a method for generating a map from path information described using natural language (textual path) is proposed. In recent years, robotics research mainly focus on vision-and-language navigation (VLN), a navigation task based on images and textual paths. Although VLN is expected to facilitate user instructions to robots, its current implementation requires users to explain the details of the path for each navigation session, which results in high explanation costs for users. To solve this problem, we proposed a method that creates a map as a topological map from a textual path and automatically creates a new path using this map. We believe that large language models (LLMs) can be used to understand textual path. Therefore, we propose and evaluate two methods, one for storing implicit maps in LLMs, and the other for generating explicit maps using LLMs. The implicit map is in the LLM's memory. It is created using prompts. In the explicit map, a topological map composed of nodes and edges is constructed and the actions at each node are stored. This makes it possible to estimate the path and actions at waypoints on an undescribed path, if enough information is available. Experimental results on path instructions generated in a real environment demonstrate that generating explicit maps achieves significantly higher accuracy than storing implicit maps in the LLMs.

CLIP feature-based randomized control using images and text for multiple tasks and robots

This study presents a control framework leveraging vision language models (VLMs) for multiple tasks and robots. Notably, existing control methods using VLMs have achieved high performance in various tasks and robots in the training environment. However, these methods incur high costs for learning control policies for tasks and robots other than those in the training environment. Considering the application of industrial and household robots, learning in novel environments where robots are introduced is challenging. To address this issue, we propose a control framework that does not require learning control policies. Our framework combines the vision-language CLIP model with a randomized control. CLIP computes the similarity between images and texts by embedding them in the feature space. This study employs CLIP to compute the similarity between camera images and text representing the target state. In our method, the robot is controlled by a randomized controller that simultaneously explores and increases the similarity gradients. Moreover, we fine-tune the CLIP to improve the performance of the proposed method. Consequently, we confirm the effectiveness of our approach through a multitask simulation and a real robot experiment using a two-wheeled robot and robot arm.