1 citations · 1 across the 12 of their papers we have counts for
14 papers
What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling
Renping Zhou, Zanlin Ni, Zihao Fan +8
World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the future must still be generated du…
Topological Necessities: Mechanism-Invariant Strategic Subgoals for Cross-Embodiment Goal-Conditioned Control
Hao Shi, Xi Li
Long-horizon goal-conditioned reinforcement learning delegates control to a high-level module that proposes subgoals, but existing subgoals are implicit byproducts of value functio…
OneLatent: Single-Token Compression for Visual Latent Reasoning
Bo Lv, Yasheng Sun, Junjie Wang +1
Chain-of-thought (CoT) prompting improves reasoning but often increases inference cost by one to two orders of magnitude. To address these challenges, we present \textbf{OneLatent}…
ExoGS: A 4D Real-to-Sim-to-Real Framework for Scalable Manipulation Data Collection
Yiming Wang, Ruogu Zhang, Minyang Li +7
Real-to-Sim-to-Real technique is gaining increasing interest for robotic manipulation, as it can generate scalable data in simulation while having narrower sim-to-real gap. However…
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…
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…
HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes
Keliang Li, Hongze Shen, Hao Shi +9
The aspiration for artificial general intelligence, fueled by the rapid progress of multimodal models, demands human-comparable performance across diverse environments. We propose…
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…
NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Weizhi Fei, Hao Shi, Jing Xu +7
Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing s…
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…
Learnware of Language Models: Specialized Small Language Models Can Do Big
Zhi-Hao Tan, Zi-Chen Zhao, Hao-Yu Shi +4
The learnware paradigm offers a novel approach to machine learning by enabling users to reuse a set of well-trained models for tasks beyond the models' original purposes. It elimin…
DenseGrounding: Improving Dense Language-Vision Semantics for Ego-Centric 3D Visual Grounding
Henry Zheng, Hao Shi, Qihang Peng +5
Enabling intelligent agents to comprehend and interact with 3D environments through natural language is crucial for advancing robotics and human-computer interaction. A fundamental…
From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation
Haoyi Shi, Mingxi Su, Ted Morris +2
This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder…