Multilingual capability is a crucial requirement for large multimodal models, which are increasingly deployed across diverse countries and languages. However, most existing benchmarks for multilingual multimodal reasoning fail to effectively distinguish models of different strengths; in fact, even text-only language models without visual capabilities can often achieve high scores. As a result, the comprehensive evaluation of state-of-the-art multilingual multimodal models remains underexplored. In this work, we present M4U, a novel and challenging benchmark designed to evaluate multilingual, multi-discipline multimodal understanding and reasoning. M4U comprises 10k samples spanning 64 disciplines across 16 subfields in Science, Engineering, and Healthcare, covering six languages. Using this benchmark, we conduct extensive evaluations of leading Large Multimodal Models (LMMs) and Large Language Models (LLMs) augmented with external tools. Our results reveal that even the strongest LMMs exhibit pronounced language preferences and struggle with reasoning tasks that require integrating multilingual information across visual and textual modalities. In particular, performance drops markedly when models are prompted with cross-lingual multimodal questions, highlighting significant gaps in current multilingual multimodal reasoning capabilities.1

This paper explores using large language models (LLMs) as low-level action planners for embodied tasks. While LLMs excel as the robot’s “brain” for high-level planning, they face challenges in directly controlling the “body” by generating precise low-level actions. This limitation arises from LLMs’ strength in high-level conceptual understanding but their inability to handle spatial perception effectively, restricting their potential in embodied tasks. To address this, we bridge the gap by enabling LLMs to not only comprehend complex instructions but also produce actionable, low-level plans. We introduce Room to Chessboard (R2C), a novel semantic representation that maps environmental states onto a grid-based chessboard, empowering LLMs to generate specific low-level coordinates and guide the robot in a manner akin to playing a game of chess. To further enhance decision-making, we propose the Chain-of-Thought Decision (CoT-D) paradigm, which improves LLMs’ interpretability and context-awareness in spatial reasoning. By jointly training LLMs for high-level task decomposition and low-level action generation, we create a unified "brain-body" system capable of handling complex, free-form instructions while producing precise low-level actions, allowing the robot to flexibly control its movements and adapt to varying tasks. We validate R2C using both fine-tuned open-source LLMs and GPT-4, demonstrating effectiveness on the challenging ALFRED benchmark. Results show that with our R2C framework, LLMs can effectively act as low-level planners, generalizing across diverse settings and open-vocabulary robotic tasks. The code and demonstrations are available at: https://vipl-vsu.github.io/Room2Chessboard.

Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitations. While several 3D reconstruction and depth completion approaches address these challenges, they suffer from setup complexity or limited observation information utilization. To address this, leveraging the power of single-view 3D object reconstruction approaches, we propose a training-free framework SR3D that enables robotic grasping of transparent and specular objects from a single-view observation. Specifically, given single-view RGB and depth images, SR3D first uses the external visual models to generate 3D reconstructed object mesh based on RGB image. Then, the key idea is to determine the 3D object’s pose and scale to accurately localize the reconstructed object back into its original depth corrupted 3D scene. Therefore, we propose view matching and keypoint matching mechanisms, which leverage both the 2D and 3D’s inherent semantic and geometric information in the observation to determine the object’s 3D state within the scene, thereby reconstructing an accurate 3D depth map for effective grasp detection. Experiments in both simulation and real-world show the reconstruction effectiveness of SR3D. More demonstrations can be found at: https://sites.google.com/view/sr3dtech/