33 citations · 56 across the 12 of their papers we have counts for
13 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…
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…
ActionSink: Toward Precise Robot Manipulation with Dynamic Integration of Action Flow
Shanshan Guo, Xiwen Liang, Junfan Lin +3
Language-instructed robot manipulation has garnered significant interest due to the potential of learning from collected data. While the challenges in high-level perception and pla…
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…
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…
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation
Xiwen Liang, Liang Ma, Shanshan Guo +4
Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots. These…
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving
Xiwen Liang, Minzhe Niu, Jianhua Han +3
Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, thes…
NLIP: Noise-robust Language-Image Pre-training
Runhui Huang, Yanxin Long, Jianhua Han +4
Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image capti…
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Xiwen Liang, Yangxin Wu, Jianhua Han +3
Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-su…
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
Xiwen Liang, Fengda Zhu, Lingling Li +2
Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. To address this problem, previous works have proposed some methods of fi…
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…
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Jianhua Han, Xiwen Liang, Hang Xu +8
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-super…
SOON: Scenario Oriented Object Navigation with Graph-based Exploration
Fengda Zhu, Xiwen Liang, Yi Zhu +2
The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual…