20 papers
From bits to atoms: A survey of cross-layer safety in Embodied AI
Tuo Feng, Yue Zhang, Shiji Zhou +35
Embodied Artificial Intelligence (AI) introduces cyber-physical risks because digital failures and attacks can cause physical harm. This survey organizes embodied AI safety through…
Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking
Tao Huang, Ruofei Liu, Xuchen Tang +15
Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance…
RoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing
Kangning Yin, Kaige Liu, Zhe Cao +9
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as b…
GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Shuai Wang, Yaxin Feng, Xuekun Jiang +12
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states a…
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
Yaping Li, Zhaxizhuoma, Qiaojun Yu +3
Articulated object manipulation requires an understanding of kinematic structure that is difficult and costly to learn from robot demonstrations alone. We introduce the Kinematic-A…
Scaling Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Kangning Yin, Xiaojie Niu +15
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making i…
MemoryWAM: Efficient World Action Modeling with Persistent Memory
Sizhe Yang, Juncheng Mu, Tianming Wei +8
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling. World action models (WAMs) posse…
EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
Ning Gao, Jinliang Zheng, Xing Gao +22
We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench comprises 26 diverse and challenging ma…
Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors
Jiahe Chen, ZiRui Wang, Feiyu Jia +7
Whole-body Humanoid-Object Interaction (HOI) is bottlenecked by the scarcity of high-fidelity 3D data. While video generative priors offer a promising alternative, existing methods…
Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM
Haifeng Huang, Yilun Chen, Zehan Wang +2
Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods struggle with fine-grained object…
Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation
Feiyu Jia, Xiaojie Niu, Sizhe Yang +5
Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remain…
Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment
Ningyu Yan, Shuai Wang, Xing Shen +4
Visuotactile sensors are indispensable for contact-rich robotic manipulation tasks. However, policy learning with tactile feedback in simulation, especially for online reinforcemen…
ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
Yang Li, Zhaxizhuoma, Hongru Jiang +11
Embodied intelligence for contact-rich manipulation has predominantly relied on position control, while explicit awareness and regulation of interaction forces remain under-explore…
One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation
Juncheng Mu, Sizhe Yang, Hojin Bae +5
Cross-embodiment manipulation is crucial for enhancing the scalability of robot manipulation and reducing the high cost of data collection. However, the significant differences bet…
Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
Tutian Tang, Xingyu Ji, Wanli Xing +7
While Vision-Language-Action (VLA) models have demonstrated remarkable success in robotic manipulation, their application has largely been confined to low-degree-of-freedom end-eff…
Embodiment-Aware Generalist Specialist Distillation for Unified Humanoid Whole-Body Control
Quanquan Peng, Yunfeng Lin, Yufei Xue +2
Humanoid Whole-Body Controllers trained with reinforcement learning (RL) have recently achieved remarkable performance, yet many target a single robot embodiment. Variations in dyn…
HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos
Yinhuai Wang, Qihan Zhao, Yuen Fui Lau +6
Enabling humanoid robots to perform agile and adaptive interactions has long been a core challenge in robotics. Current approaches are bottlenecked by either the scarcity of realis…
Scalable and General Whole-Body Control for Cross-Humanoid Locomotion
Yufei Xue, YunFeng Lin, Wentao Dong +6
Learning-based whole-body controllers have become a key driver for humanoid robots, yet most existing approaches require robot-specific training. In this paper, we study the proble…
RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing
Kangning Yin, Zhe Cao, Wentao Dong +7
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a major challenge, particularly in contact-rich and highly dynamic tasks such as boxi…
UniCon: A Unified System for Efficient Robot Learning Transfers
Yunfeng Lin, Li Xu, Yong Yu +2
Deploying learning-based controllers across heterogeneous robots is challenging due to platform differences, inconsistent interfaces, and inefficient middleware. To address these i…