2 citations · 3 across the 28 of their papers we have counts for
19 papers · 1 filter
Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
Zhihao Sun, Liu Liu, Xinjiang Wang +6
Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision…
Rethinking Representations for World-Action Modeling
Haoyi Jiang, Liu Liu, Xinjiang Wang +12
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design o…
CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces
Sen Wang, Liu Liu, Xinjiang Wang +7
Robot policies increasingly incorporate semantic reasoning and future-world prediction, yet combining these capabilities does not guarantee that local predictions and actions remai…
InstructMove: A Text-Indispensable Benchmark for Instruction-Following Manipulation
Mengao Zhao, Ziang Li, Chaodong Huang +15
Vision-language-action (VLA) models have made general-purpose robot manipulation increasingly plausible by conditioning robot actions on natural-language instructions. A key test o…
EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI
Xinjie Wang, Liu Liu, Taojun Ding +9
We present EmbodiedGen V2, a generative 3D world engine for building executable policy-ready environments for embodied intelligence. Sim-ready 3D asset generation has advanced rapi…
DreamWAM: Beyond RGB Future Prediction for World Action Models
Shanglin Yuan, Weiheng Zhao, Xin Shi +6
World Action Models (WAMs) learn action-relevant representations by predicting how the observed world will evolve. Most existing WAMs define this future in RGB space, where task-re…
Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models
Weiheng Zhao, Haoyi Jiang, Xin Shi +5
World Action Models (WAMs) improve robot manipulation by learning how the environment evolves beyond the current observation. However, existing approaches face a fundamental dilemm…
IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer
Zhengyu Zou, Hao Li, Kuixuan Jiao +7
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spa…
NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction
Xiangyu Sun, Liu Liu, Seungkwon Yang +4
Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance degrades significantly in lo…
GeoFlow-SLAM++: A Robust Multi-Camera Visual-Inertial SLAM System with Relocalization
Wei Feng, Tingyang Xiao, Liu Liu +2
Monocular and RGB-D visual-inertial SLAM systems remain susceptible to limited field of view, sensor-specific failure modes, and unreliable cross-session relocalization. To address…
HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory
Xiaolin Zhou, Liu Liu, Tingyang Xiao +9
LLM agents follow a practical execution loop in digital environments: they reason over structured states, invoke tools, inspect feedback, and revise actions. Extending this loop to…
Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning
Yukai Ma, Joe Lin, Liu Liu +5
Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe navigation of mobile robots such as…
SimWeaver: Zero-Shot RGB Sim-to-Real for Deformable Manipulation
Wenkang Hu, Haoran Wang, Yitong Li +10
RGB sim-to-real for deformable manipulation has remained largely unsolved without real-world fine-tuning. We present SimWeaver, which trains zero-shot RGB VLA policies on 200 simul…
ManiSplat: Manipulation Trajectory Synthesis from Monocular Video via Decoupled 3D Gaussian Splatting
Wenhao Hu, Haonan Zhou, Liu Liu +5
Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics. While recent advances in 3D Gaussian…
Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
Youzhi Liu, Li Gao, Yifei Qian +3
Quadruped robots have achieved remarkable locomotion, yet their behavioral repertoire remains confined to a few gaits--far from the expressive, companion-like presence long envisio…
3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image
Ze-Xin Yin, Liu Liu, Xinjie Wang +4
Compositional 3D scene generation from a single view requires the simultaneous recovery of scene layout and 3D assets. Existing approaches mainly fall into two categories: feed-for…
IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping
Tingyang Xiao, Liu Liu, Wei Feng +6
Geometry foundation models have significantly advanced dense geometric SLAM, yet existing systems often lack deep semantic understanding and robust loop closure capabilities. Meanw…
Hybrid Fusion: One-Minute Efficient Training for Zero-Shot Cross-Domain Image Fusion
Ran Zhang, Xuanhua He, Liu Liu
Image fusion seeks to integrate complementary information from multiple sources into a single, superior image. While traditional methods are fast, they lack adaptability and perfor…
Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning
Haoyi Jiang, Liu Liu, Xinjie Wang +5
Vision-language models excel at 2D visual understanding but remain limited in 3D spatial reasoning. Existing approaches either depend on explicit 3D modalities, which limits scalab…