20 citations · 86 across the 20 of their papers we have counts for
23 papers
RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models
Weijia Liufu, Xiaoyu Guo, Ruiyi Chen +16
Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while…
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
Yunshuang Nie, Bingqian Lin, Minzhe Niu +7
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to so…
EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation
Min Lin, Xiwen Liang, Bingqian Lin +12
Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. However, existing VLAs are mostly…
Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
Kun Xiang, Terry Jingchen Zhang, Yinya Huang +13
The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical perception and symbolic physics rea…
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
Liang Ma, Jiajun Wen, Min Lin +12
While vision-language models (VLMs) have demonstrated promising capabilities in reasoning and planning for embodied agents, their ability to comprehend physical phenomena, particul…
EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning
Bingqian Lin, Yunshuang Nie, Khun Loun Zai +10
Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) pe…
MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents
Ziming Wei, Bingqian Lin, Zijian Jiao +5
Spatial Planning is a crucial part in the field of spatial intelligence, which requires the understanding and planning about object arrangements in space perspective. AI agents wit…
Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language Navigation
Ziming Wei, Bingqian Lin, Yunshuang Nie +4
Data scarcity is a long-standing challenge in the Vision-Language Navigation (VLN) field, which extremely hinders the generalization of agents to unseen environments. Previous work…
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning
Xiwen Liang, Min Lin, Weiqi Ruan +6
Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments. These challenges…
PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation
Kaidong Zhang, Pengzhen Ren, Bingqian Lin +4
Language-guided robotic manipulation is a challenging task that requires an embodied agent to follow abstract user instructions to accomplish various complex manipulation tasks. Pr…
Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation
Jiaqi Chen, Bingqian Lin, Xinmin Liu +3
LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) task. However, existing LLM-based methods often focus only on solving high-l…
Correctable Landmark Discovery via Large Models for Vision-Language Navigation
Bingqian Lin, Yunshuang Nie, Ziming Wei +5
Vision-Language Navigation (VLN) requires the agent to follow language instructions to reach a target position. A key factor for successful navigation is to align the landmarks imp…
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
Bingqian Lin, Yanxin Long, Yi Zhu +4
Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN…
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning
Bingqian Lin, Yunshuang Nie, Ziming Wei +6
Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural languag…
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
Jiaqi Chen, Bingqian Lin, Ran Xu +3
Embodied agents equipped with GPT as their brains have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents…
Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
Bingqian Lin, Zicong Chen, Mingjie Li +13
Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks, which is very practical in the medical domain. It can significantly re…
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
Mingjie Li, Bingqian Lin, Zicong Chen +3
Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced da…
Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation
Bingqian Lin, Yi Zhu, Xiaodan Liang +2
Vision-Language Navigation (VLN) is a challenging task which requires an agent to align complex visual observations to language instructions to reach the goal position. Most existi…
ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts
Bingqian Lin, Yi Zhu, Zicong Chen +3
Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially…
Contrastive Instruction-Trajectory Learning for Vision-Language Navigation
Xiwen Liang, Fengda Zhu, Yi Zhu +3
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step follo…
Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation
Bingqian Lin, Yi Zhu, Yanxin Long +3
Language instruction plays an essential role in the natural language grounded navigation tasks. However, navigators trained with limited human-annotated instructions may have diffi…
Vision-Dialog Navigation by Exploring Cross-modal Memory
Yi Zhu, Fengda Zhu, Zhaohuan Zhan +4
Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help w…
Jointly Deep Multi-View Learning for Clustering Analysis
Bingqian Lin, Yuan Xie, Yanyun Qu +2
In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignmen…