Tiancai Wang

Feeling the Unexpected: ResTacVLA for Contact-Rich Manipulation via Residual Tactile Representation

Pengwei Zhang, Bin Xie, Xinpan Meng +5

Tactile perception is indispensable for contact-rich manipulation, yet integrating it into Vision-Language-Action (VLA) models often induces modality collapse, where high-bandwidth…

MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models

Hao Shi, Weiye Li, Bin Xie +6

Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models r…

QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation

Yuxiang Chen, Yuanhao Wang, Ziheng Zhang +6

Simulation is central to robot learning, yet the sim-to-real gap remains a major bottleneck. Existing approaches often tackle visual or dynamic gaps separately, overlooking how the…

UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms

Yufei Jia, Zhanxiang Cao, Mingrui Yu +48

Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-ce…

Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs

Jiahui Niu, Kefan Gu, Yucheng Zhao +5

Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by the high latency of full infe…

PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models

Xinyu Guo, Bin Xie, Wei Chai +4

Large-scale pretraining has made Vision-Language-Action (VLA) models promising foundations for generalist robot manipulation, yet adapting them to downstream tasks remains necessar…

GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning

Yufei Jia, Heng Zhang, Ziheng Zhang +39

Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based loco…

Robotic Scene Cloning:Advancing Zero-Shot Robotic Scene Adaptation in Manipulation via Visual Prompt Editing

Binyuan Huang, Yuqing Wen, Yucheng Zhao +5

Modern robots can perform a wide range of simple tasks and adapt to diverse scenarios in the well-trained environment. However, deploying pre-trained robot models in real-world use…

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

En Yu, Haoran Lv, Jianjian Sun +46

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed…

SpatialActor: Exploring Disentangled Spatial Representations for Robust Robotic Manipulation

Hao Shi, Bin Xie, Yingfei Liu +5

Robotic manipulation requires precise spatial understanding to interact with objects in the real world. Point-based methods suffer from sparse sampling, leading to the loss of fine…

Running VLAs at Real-time Speed

Yunchao Ma, Yizhuang Zhou, Yunhuan Yang +2

In this paper, we show how to run pi0-level multi-view VLA at 30Hz frame rate and at most 480Hz trajectory frequency using a single consumer GPU. This enables dynamic and real-time…

Dexbotic: Open-Source Vision-Language-Action Toolbox

Bin Xie, Erjin Zhou, Fan Jia +36

In this paper, we present Dexbotic, an open-source Vision-Language-Action (VLA) model toolbox based on PyTorch. It aims to provide a one-stop VLA research service for professionals…

ManiAgent: An Agentic Framework for General Robotic Manipulation

Yi Yang, Kefan Gu, Yuqing Wen +4

While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning i…

IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction

Yandu Chen, Kefan Gu, Yuqing Wen +3

Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control, offering a promising path toward general-purpose em…

MUVLA: Learning to Explore Object Navigation via Map Understanding

Peilong Han, Fan Jia, Min Zhang +5

In this paper, we present MUVLA, a Map Understanding Vision-Language-Action model tailored for object navigation. It leverages semantic map abstractions to unify and structure hist…

LLaDA-VLA: Vision Language Diffusion Action Models

Yuqing Wen, Hebei Li, Kefan Gu +3

The rapid progress of auto-regressive vision-language models (VLMs) has inspired growing interest in vision-language-action models (VLA) for robotic manipulation. Recently, masked…

GeoVLA: Empowering 3D Representations in Vision-Language-Action Models

Lin Sun, Bin Xie, Yingfei Liu +3

Vision-Language-Action (VLA) models have emerged as a promising approach for enabling robots to follow language instructions and predict corresponding actions. However, current VLA…

MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation

Hao Shi, Bin Xie, Yingfei Liu +7

Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horiz…

RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping

Dongming Wu, Yanping Fu, Saike Huang +8

General robotic grasping systems require accurate object affordance perception in diverse open-world scenarios following human instructions. However, current studies suffer from th…

Hita: Holistic Tokenizer for Autoregressive Image Generation

Anlin Zheng, Haochen Wang, Yucheng Zhao +4

Vanilla autoregressive image generation models generate visual tokens step-by-step, limiting their ability to capture holistic relationships among token sequences. Moreover, becaus…

Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation

Anlin Zheng, Xin Wen, Xuanyang Zhang +5

In this work, we present a novel direction to build an image tokenizer directly on top of a frozen vision foundation model, which is a largely underexplored area. Specifically, we…

ROSA: Harnessing Robot States for Vision-Language and Action Alignment

Yuqing Wen, Kefan Gu, Haoxuan Liu +4

Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Langua…

DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework

Yani Zhang, Dongming Wu, Hao Shi +3

A core task in embodied intelligence is ego-centric 3D visual grounding. Existing methods typically adopt two-stage, heterogeneous pipelines that pair a detector with a separate gr…