2 citations · 3 across the 11 of their papers we have counts for
12 papers
sGLP: Simulating People with Stochastic Agents
Ehud Shapiro
Grassroots platforms are designed to be owned, operated and governed by their participants, and thus are constituted from person-to-person smartphone apps that are run by the parti…
-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills
Siyao Xiao, Yuhong Zhang, Zhifang Liu +9
Current Vision-Language-Action (VLA) models predominantly rely on end-to-end fine-tuning. While effective, this paradigm compromises the inherent generalization capabilities of Vis…
RynnBrain: Open Embodied Foundation Models
Ronghao Dang, Jiayan Guo, Bohan Hou +23
Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reas…
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
Ke Zhang, Lixin Xu, Chengyi Song +4
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major…
RoboWheel: A Data Engine from Real-World Human Demonstrations for Cross-Embodiment Robotic Learning
Yuhong Zhang, Zihan Gao, Shengpeng Li +12
We introduce Robowheel, a data engine that converts human hand object interaction (HOI) videos into training-ready supervision for cross morphology robotic learning. From monocular…
Emergence of Spatial Representation in an Actor-Critic Agent with Hippocampus-Inspired Sequence Generator
Xiao-Xiong Lin, Yuk-Hoi Yiu, Christian Leibold
Sequential firing of hippocampal place cells is often attributed to sequential sensory drive along a trajectory, and has also been attributed to planning and other cognitive functi…
GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert
Mingyu Liu, Zheng Huang, Xiaoyi Lin +6
Vision-language models demonstrate strong reasoning and planning abilities, yet grounding these predictions into precise robot actions remains a central challenge. Existing Vision-…
NoTVLA: Semantics-Preserving Robot Adaptation via Narrative Action Interfaces
Zheng Huang, Mingyu Liu, Xiaoyi Lin +9
Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic fo…
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Yang Chen, Xiao Lin, Bo Yan +4
Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games…
General-Purpose Aerial Intelligent Agents Empowered by Large Language Models
Ji Zhao, Xiao Lin
The emergence of large language models (LLMs) opens new frontiers for unmanned aerial vehicle (UAVs), yet existing systems remain confined to predefined tasks due to hardware-softw…
AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation
Taimeng Fu, Zitong Zhan, Zhipeng Zhao +7
Off-road navigation is critical for a wide range of field robotics applications from planetary exploration to disaster response. However, it remains a longstanding challenge due to…
SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
Abhinav Rajvanshi, Karan Sikka, Xiao Lin +3
Semantic reasoning and dynamic planning capabilities are crucial for an autonomous agent to perform complex navigation tasks in unknown environments. It requires a large amount of…