Papers with robotics
Tutorial on Multimodal Machine Learning (2022.naacl-tutorials)
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| Challenge: | Multimodal machine learning is a challenging but crucial area with numerous applications in multimedia, affective computing, robotics, finance, HCI, and healthcare. |
| Approach: | This tutorial will describe an updated taxonomy on multimodal machine learning synthesizing its core technical challenges and major directions for future research. |
| Outcome: | The proposed taxonomy synthesizes the core technical challenges and major directions for future research. |
LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning (2024.findings-emnlp)
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| Challenge: | Existing path planning algorithms suffer from significant computational and memory inefficiencies as the state space grows . large language models excel in environmental analysis but fall short in detailed spatial and temporal reasoning . |
| Approach: | They propose a new path planning method that synergistically combines A* and LLMs to improve pathfinding efficiency. |
| Outcome: | The proposed method improves pathfinding efficiency while maintaining integrity of path validity in large-scale scenarios. |
Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification (2024.emnlp-main)
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| Challenge: | Emotion classification is an important task with applications in education, virtual reality, and robotics. |
| Approach: | They propose to use token embeddings to generate a "semantic-anchor graph" using semantic anchors, sentences can be projected onto them to form a graph . |
| Outcome: | Empirically, the proposed system can generate meaningful semantic anchors and discriminative graph patterns for different emotion. |
A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)
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Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers. |
| Approach: | They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment. |
| Outcome: | The proposed model enables finer credit assignment, richer diagnostics, and improved robustness. |
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021.eacl-main)
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| Challenge: | a lack of transparency is a key issue for robotics and AI. |
| Approach: | They propose to map existing automatic evaluation methods for natural language generation onto explanations. |
| Outcome: | The proposed model shows that embedding-based evaluation methods have higher correlations with human ratings than word-overlap metrics. |
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator (2025.emnlp-main)
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| Challenge: | Existing robot simulators focus on physical process modeling and realistic rendering, resulting in high computational costs and limited adaptability. |
| Approach: | They propose a modular and novel LLM-powered framework to analyze and validate robot behaviors in text-based environments. |
| Outcome: | The proposed framework can generalize across scenarios and achieve long-horizon complex simulation. |
A Scientific Information Extraction Dataset for Nature Inspired Engineering (2020.lrec-1)
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| Challenge: | Existing approaches to extract relevant biological information from scientific literature are difficult and require domain-specific knowledge. |
| Approach: | They describe a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text. |
| Outcome: | The proposed dataset allows for training and evaluation of Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers. |
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)
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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji
| Challenge: | a framework for constructing dialogue world models for natural language tasks is currently lacking. |
| Approach: | They propose a framework that can be used to train a dialogue world model. |
| Outcome: | The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification . |
Exploring Spatial Schema Intuitions in Large Language and Vision Models (2024.findings-acl)
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| Challenge: | Large language models excel in varied NLP tasks, but lack a direct connection between sensory perception and physical action. |
| Approach: | They examine whether large language models capture implicit human intuitions about building blocks of language . they employ spatial cognitive foundations developed through early sensorimotor experiences . |
| Outcome: | The proposed model captures implicit human intuitions about building blocks of language without a tangible connection to embodied experiences. |
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
| Approach: | They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments. |
| Outcome: | This paper reviews current studies in the emerging field of vision-and-language navigation . it highlights limitations and opportunities for future work . |
Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge (2025.findings-acl)
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| Challenge: | Temporal Logic (STL) is a formal specification tool for cyber-physical systems . but it is difficult to transform ambiguous and complex data into STL, a paper argues . |
| Approach: | They propose a NL-STL dataset with 16,000 samples enriched with diverse patterns . they propose KGST framework to transform natural language into STL using a generate-then-refine process . |
| Outcome: | The proposed dataset outperforms baseline models in diversity and accuracy . the proposed dataset contains 16,000 samples enriched with diverse patterns . |
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts (2023.findings-emnlp)
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| Challenge: | Visual question answering (VQA) is a task that requires an understanding of both the image and the question to provide a natural language answer. |
| Approach: | They propose a multimodal framework that leverages language guidance to answer questions more accurately. |
| Outcome: | The proposed framework improves on the multi-choice question-answering task using CLIP and BLIP models. |
DetGPT: Detect What You Need via Reasoning (2023.emnlp-main)
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Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang
| Challenge: | Recent advances in the field of computer vision have enabled more effective and sophisticated interactions between humans and machines. |
| Approach: | They propose a reasoning-based object detection paradigm that leverages state-of-the-art multi-modal models and open-vocabulary object detectors to perform reasoning within the context of the user’s instructions and the visual scene. |
| Outcome: | The proposed method enables users to interact with the system using natural language instructions, allowing for a higher level of interactivity. |
CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook (2026.findings-acl)
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| Challenge: | Multimodal representation alignment is crucial for large language models and robotics. |
| Approach: | They propose a framework that optimizes multimodal representation spaces through a modality-shared-specific codebook design. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multimodal classification and retrieval tasks. |
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)
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Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation. |
| Approach: | They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives. |
| Outcome: | The proposed model performs well on scene graph understanding but struggles with scene graph generation, particularly for complex narratives. |
DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Robotics (2025.acl-long)
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| Challenge: | Experimental results show that Dynamic Retrieval-Augmented Expert Networks outperforms baseline approaches in long-term task retention and knowledge reuse. |
| Approach: | They propose a dynamic routing architecture that leverages MoE and Retrieval-Augmented Generation to augment the learning process. |
| Outcome: | The proposed architecture outperforms baseline approaches in long-term task retention and knowledge reuse. |
Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions (2024.emnlp-main)
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| Challenge: | Building socially-intelligent AI agents involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents. |
| Approach: | They propose a set of technical challenges and open questions for researchers to advance Social-AI. |
| Outcome: | The proposed frameworks are based on the social intelligence competencies that evolved over thousands of years in Homo sapiens and are expected to be the foundations for the development of social-intelligent AI agents. |
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning (2025.acl-long)
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Zifu Wan, Yaqi Xie, Ce Zhang, Zhiqiu Lin, Zihan Wang, Simon Stepputtis, Deva Ramanan, Katia P. Sycara
| Challenge: | Large multimodal foundation models perceive objects as indivisible, overlooking the components that constitute them. |
| Approach: | They propose a novel benchmark for large multimodal foundation models comprising hand-labeled part segmentation annotations and task-oriented instructions to evaluate their performance. |
| Outcome: | The proposed benchmark improves performance of current models in understanding and executing part-level tasks within everyday contexts. |