4 citations · 11 across the 17 of their papers we have counts for
17 papers
HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
Rui Zhong, Yizhe Sun, Junjie Wen +7
A significant bottleneck in humanoid policy learning is the acquisition of large-scale, diverse datasets, as collecting reliable real-world data remains both difficult and cost-pro…
ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
Qiyuan Zeng, Chengmeng Li, Jude St. John +5
We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation. ActiveUMI couples…
dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought
Junjie Wen, Minjie Zhu, Jiaming Liu +6
Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics. We introduce dVLA, a diffusion-based VLA that leverages a multimodal chain-of-thought t…
ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge
Zhongyi Zhou, Yichen Zhu, Junjie Wen +2
Vision-language-action (VLA) models have emerged as the next generation of models in robotics. However, despite leveraging powerful pre-trained Vision-Language Models (VLMs), exist…
WorldEval: World Model as Real-World Robot Policies Evaluator
Yaxuan Li, Yichen Zhu, Junjie Wen +2
The field of robotics has made significant strides toward developing generalist robot manipulation policies. However, evaluating these policies in real-world scenarios remains time…
PointVLA: Injecting the 3D World into Vision-Language-Action Models
Chengmeng Li, Junjie Wen, Yan Peng +3
Vision-Language-Action (VLA) models excel at robotic tasks by leveraging large-scale 2D vision-language pretraining, but their reliance on RGB images limits spatial reasoning criti…
ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration
Minjie Zhu, Yichen Zhu, Jinming Li +6
Imitation learning has proven to be highly effective in teaching robots dexterous manipulation skills. However, it typically relies on large amounts of human demonstration data, wh…
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
Zhongyi Zhou, Yichen Zhu, Minjie Zhu +8
Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can't large language models replicate this holistic understanding? Thr…
DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
Junjie Wen, Yichen Zhu, Jinming Li +3
Enabling robots to perform diverse tasks across varied environments is a central challenge in robot learning. While vision-language-action (VLA) models have shown promise for gener…
CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance
Jinming Li, Yichen Zhu, Zhibin Tang +8
Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving…
Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning
Junjie Wen, Minjie Zhu, Yichen Zhu +8
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our…
Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation
Kun Wu, Yichen Zhu, Jinming Li +4
Learning visuomotor policy for multi-task robotic manipulation has been a long-standing challenge for the robotics community. The difficulty lies in the diversity of action space:…
Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation
Minjie Zhu, Yichen Zhu, Jinming Li +8
Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep…
TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
Junjie Wen, Yichen Zhu, Jinming Li +9
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA…
A Survey on Robotics with Foundation Models: toward Embodied AI
Zhiyuan Xu, Kun Wu, Junjie Wen +4
While the exploration for embodied AI has spanned multiple decades, it remains a persistent challenge to endow agents with human-level intelligence, including perception, learning,…
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
Minjie Zhu, Yichen Zhu, Jinming Li +8
The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recogniti…
Object-Centric Instruction Augmentation for Robotic Manipulation
Junjie Wen, Yichen Zhu, Minjie Zhu +8
Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as \enquote{pick and place}, understand…