22 papers
EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ
Yunsong Zhou, Xuekun Jiang, Haiqi Shen +11
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold. Scaling collection…
SocialHumanoid: Towards Expressive Humanoid Behavior via One-Step Co-Speech Motion Generation
Chengqun Yang, Tengjie Zhu, Liang Xu +10
Humanoid robots are increasingly expected to serve as embodied social agents that communicate naturally with humans through face-to-face interaction. During such communication, hum…
InternW0: A Foundational Physical World Model for Efficient Real-World Interactions
Jisong Cai, Yao Mu, Ganlin Yang +23
Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We introduce InternW0, the first…
GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models
Zijian Zhang, Yuqing Jiang, Weitao Zhou +6
World-Action Models (WAMs) jointly learn future visual prediction and action generation, using video dynamics as a representation-learning signal for robotic manipulation. However,…
Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control
Weili Zeng, Yitong Xing, Fulong Liu +10
World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a costly generative branch. We ar…
Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation
Boyu Mi, Mengchen Ma, Yifei Yao +10
Real-world deployment of embodied agents requires active exploration, visual grounding, and interactive intent disambiguation. However, existing frameworks often rely on privileged…
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…
DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
Zili Lin, Wenyao Zhang, Yuyang Zhang +9
Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation due to two challenges: (1) the…
LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models
Rongxu Cui, Zongzheng Zhang, Jingrui Pang +11
Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains largely unverified. To address th…
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Jiaxiang Liu, Tianxiang Hu, Juwei Guan +5
Recent advances in vision-language models (VLMs) such as CLIP have demonstrated strong generalization across natural-image domains. However, adapting these models to biomedical ima…
Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
Bin Qiu, Yanming Shao, Guanyu Cai +1
Reinforcement learning can train bimanual dexterous hands to play piano in physics simulation with high note accuracy, but for high-DoF dexterous hands, relying solely on task rewa…
dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models
Yuhao Wu, Yitian Liu, Weijie Shen +13
Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reasoning of VLMs. Prevailing ar…
ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Yuyang Zhang, Wenyao Zhang, Zekun Qi +7
World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense mult…
ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Wei Xiao, Weiliang Tang, Yuying Ge +4
Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models. However, enabling seamless humanoid interventions is a formidable syst…
V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos
Kaihan Chen, Yanming Shao, Haifeng Ji +2
Achieving autonomous robotic dexterous manipulation requires precise, human-like action sequences at scale. As a scalable supplement to costly teleoperation data, extracting trajec…
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…
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
Zemin Yang, Yaoyu He, Yiming Zhong +5
Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent…
Dexora: Open-source VLA for High-DoF Bimanual Dexterity
Zongzheng Zhang, Jingrui Pang, Zhuo Yang +22
Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper control or single-arm dextero…
DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
Hanwen Wang, Weizhi Zhao, Xiangyu Wang +11
Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands standardized benchmarks for…
-EqM: Equilibrium Matching for Closed-Loop Vision-Language-Action Control
Huanming Liu, Congsheng Xu, Jianmin Ji +1
Currently, Vision-Language-Action (VLA) models have become the most adopted paradigm for robotic manipulation for its great potential for task generalization. While most generative…
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Haozhan Li, Yuxin Zuo, Jiale Yu +18
Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and super…
RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete
Yuheng Ji, Huajie Tan, Jiayu Shi +14
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenari…