2 citations · 2 across the 21 of their papers we have counts for
23 papers
SimpleTouch: Can Vision-Language-Action Models Master Contact-Rich Manipulation Without Tactile Policy Pretraining?
Chen Yang, Linzhe Shi, Changjie Wu +7
Tactile sensing provides essential contact information for robotic manipulation, yet incorporating it into pretrained vision-language-action (VLA) models remains challenging. A com…
ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
Mingxin Wang, Bin Hu, Bin Qian +12
World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance on pixel-generative future sup…
DLAM: Distributional Latent Actions with Temporal Constraints
Zuojin Tang, Feifan Luo, Haoyun Liu +10
Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change. Latent action…
ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
Ronghan Chen, Yandan Yang, Zuojin Tang +18
Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack expl…
ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
Zuojin Tang, Haoyun Liu, Xinyuan Chang +11
Vision-language-action (VLA) models remain constrained by the scarcity of action-labeled robot data, whereas action-free videos provide abundant evidence of how the physical world…
Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation
Junjin Xiao, Dongyang Li, Yandan Yang +9
This paper tackles spatial perception and manipulation challenges in Vision-Language-Action (VLA) models. To address depth ambiguity from monocular input, we leverage a pre-trained…
Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation
Qiuyu Ding, Heng-Da Xu, Wei Zhang +4
Generative point-of-interest (POI) recommendation models based on large language models (LLMs) have shown promising results by formulating next POI prediction as a sequence generat…
DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
Lingjun Zhang, Changjie Wu, Linzhe Shi +6
End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual reasoning to enhance the robust…
ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents
Dongjie Huo, Haoyun Liu, Guoqing Liu +10
Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environments. Although Vision-Language…
ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment
Yuzhi Chen, Ronghan Chen, Dongjie Huo +11
Video-based world models offer a powerful paradigm for embodied simulation and planning, yet state-of-the-art models often generate physically implausible manipulations - such as o…
Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models
Haoyun Liu, Jianzhuang Zhao, Xinyuan Chang +11
Despite the rapid progress of vision-language-action (VLA) models, the prevailing practice of predicting action chunks as discrete waypoints remains structurally misaligned with th…
MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving
Lingjun Zhang, Yujian Yuan, Changjie Wu +7
Vision-Language Models (VLM) exhibit strong reasoning capabilities, showing promise for end-to-end autonomous driving systems. Chain-of-Thought (CoT), as VLM's widely used reasonin…
ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
Yandan Yang, Shuang Zeng, Tong Lin +11
Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ''one-brain, many-forms'' paradigm. Progress is hinder…
MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation
Dekang Qi, Shuang Zeng, Xinyuan Chang +4
Visual Language Navigation (VLN) is one of the fundamental capabilities for embodied intelligence and a critical challenge that urgently needs to be addressed. However, existing me…
AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction
Ruikai Li, Xinrun Li, Mengwei Xie +12
Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a cr…
Seeing Space and Motion: Enhancing Latent Actions with Geometric and Dynamic Awareness for Vision-Language-Action Models
Zhejia Cai, Yandan Yang, Xinyuan Chang +5
Latent Action Models (LAMs) enable Vision- Language-Action (VLA) systems to learn semantic action representations from large-scale unannotated data. Yet, we identify two bottleneck…
World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
Junjin Xiao, Yandan Yang, Xinyuan Chang +5
Vision-Language-Action (VLA) models trained via imitation learning suffer from significant performance degradation in data-scarce scenarios due to their reliance on large-scale dem…
Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Shiyi Liang, Xinyuan Chang, Changjie Wu +8
Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, exist…
JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation
Shuang Zeng, Dekang Qi, Xinyuan Chang +7
Vision-and-Language Navigation requires an embodied agent to navigate through unseen environments, guided by natural language instructions and a continuous video stream. Recent adv…
UniMapGen: A Generative Framework for Large-Scale Map Construction from Multi-modal Data
Yujian Yuan, Changjie Wu, Xinyuan Chang +6
Large-scale map construction plays a vital role in applications like autonomous driving and navigation systems. Traditional large-scale map construction approaches mainly rely on c…
SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
Mengwei Xie, Shuang Zeng, Xinyuan Chang +4
Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures-such as loops and bidirectional lanes-preva…
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving
Shuang Zeng, Xinyuan Chang, Mengwei Xie +6
Vision-Language-Action (VLA) models offer significant potential for end-to-end driving, yet their reasoning is often constrained by textual Chains-of-Thought (CoT). This symbolic c…
PriorDrive: Enhancing Online HD Mapping with Unified Vector Priors
Shuang Zeng, Xinyuan Chang, Xinran Liu +5
High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles, yet their creation and upkeep present significant cost and timel…