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  • [2502. 09560] EmbodiedBench: Comprehensive Benchmarking Multi-modal . . .
    Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks To bridge this gap, we introduce EmbodiedBench, an extensive benchmark designed
  • Foundation Model Driven Robotics: A Comprehensive Review
    The rapid emergence of foundation models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), has introduced a transformative paradigm in robotics These models offer powerful capabilities in semantic understanding, high-level reasoning, and cross-modal generalization, enabling significant advances in perception, planning, control, and human-robot interaction This
  • GitHub - EmbodiedBench EmbodiedBench: [ICML 2025 Oral] Official repo of . . .
    We introduce EmbodiedBench, a comprehensive benchmark designed to evaluate Multi-modal Large Language Models (MLLMs) as embodied agents While existing benchmarks have primarily focused on Large Language Models (LLMs) and high-level tasks, EmbodiedBench takes a leap forward by offering a comprehensive, fine-grained evaluation of MLLM-based agents across both high-level and low-level tasks, as
  • EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language . . .
    We introduce EmbodiedBench, a comprehensive benchmark designed to evaluate Multi-modal Large Language Models (MLLMs) as embodied agents While existing benchmarks have primarily focused on Large Language Models (LLMs) and high-level tasks, EmbodiedBench takes a leap forward by offering a comprehensive, fine-grained evaluation of MLLM-based agents across both high-level and low-level tasks, as
  • Open-source vision-language-action models for robotics
    In recent years, Vision-Language-Action (VLA) foundation models have been advancing embodied intelligence in robotics by integrating multimodal perception, semantic understanding, and dynamic action generation through end-to-end architectures This paper focuses on open-source VLA models and their technological innovations and practical applications across three representative robotic domains
  • EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language . . .
    Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks To bridge this gap, we introduce EmbodiedBench, an extensive benchmark designed
  • Survey on Foundation Models for Embodied Decision Making
    Foundation models are reshaping embodied AI – from robots that manipulate the physical world to agents that navigate virtual environments These models leverage vast datasets and high-capacity architectures to learn generalizable policies for perception, reasoning, and action across diverse tasks and domains This survey reviews recent advances in vision-language-action (VLA)
  • NeurIPS Competition Foundation Models for Embodied Agents
    The competition aims to advance the understanding of how LLMs reason in embodied environments, promote the development of robust and interpretable LLM AI agents, and foster collaboration between the language modeling and robotics communities More info at https: neurips25-eai github io
  • A Survey on Robotics with Foundation Models: toward Embodied AI
    This survey aims to provide a comprehensive and up-to-date overview of foundation models in robotics, focusing on autonomous manipulation and encompassing high-level planning and low-level control Moreover, we showcase their commonly used datasets, simulators, and benchmarks
  • Robot learning in the era of foundation models: a survey
    The proliferation of large language models (LLMs) has fueled a shift in robot learning from automation towards general embodied artificial intelligence (AI) Adopting foundation models together with traditional learning methods for robot learning has increasingly gained interest in the research community and shown potential for real-life application However, there is little literature that
  • ECBench: Can Multi-modal Foundation Models Understand the Egocentric . . .
    A comprehensive and reliable benchmark is essential for evaluating the multimodal understanding capabilities of models in egocentric embodied contexts Such bench-marks are very scarce due to the challenges involved in egocentric video collection and embodied questions annota-tion





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