15 citations · 16 across the 5 of their papers we have counts for
12 papers
PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation
Xinyu Guo, Chao Jing, Li Yi +2
Generalist robot manipulation can benefit from pretrained vision-language models (VLMs) for semantic reasoning and video generation models (VGMs) for world modeling. However, exist…
Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies
Galbot Team, Xuchuan Chen, Xiaoqian Cheng +31
GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embo…
RoboGesture: Real-Time Semantic-aligned Co-Speech Gestures Generation for Humanoid Interaction
Zifan Wang, Ziang Ren, Pengyang Shi +7
Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot interaction. However, this task fac…
HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark
Dairu Liu, Zekun Qi, Jiayu Zeng +11
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-fram…
LIMMT: Less is More for Motion Tracking
Yu Guan, Zekun Qi, Chenghuai Lin +7
We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce LIMMT (Less Is More for Mot…
Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Zekun Qi, Xuchuan Chen, Dairu Liu +10
We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrai…
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Wenyao Zhang, Hongsi Liu, Zekun Qi +11
Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot ma…
DexVLG: Dexterous Vision-Language-Grasp Model at Scale
Jiawei He, Danshi Li, Xinqiang Yu +7
As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection,…
OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
Mengdi Jia, Zekun Qi, Shaochen Zhang +5
Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or impr…
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
Zekun Qi, Wenyao Zhang, Yufei Ding +15
While spatial reasoning has made progress in object localization relationships, it often overlooks object orientation-a key factor in 6-DoF fine-grained manipulation. Traditional p…
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Zekun Qi, Runpei Dong, Shaochen Zhang +5
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clou…
DreamLLM: Synergistic Multimodal Comprehension and Creation
Runpei Dong, Chunrui Han, Yuang Peng +11
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multi…