1 citations · 1 across the 26 of their papers we have counts for
37 papers
LocoManiLab: A Unified Testbed for Data, Policies, Controllers, and Deployment in Humanoid Loco-Manipulation
Dewei Wang, Jinrui Han, Jiyuan Shi +9
Humanoid robots must coordinate locomotion and manipulation in everyday environments, yet progress is difficult to assess across differing policies, controllers, and task settings.…
KODA: Kinematics--Observation Data Alignment for Humanoid Learning from Human Egocentric Demonstration
Zibo Zhou, Yueru Chen, Junsong Wu +8
Training humanoid whole-body vision-language-action (VLA) policies requires costly robot demonstrations with aligned robot-view observations and controller-compatible whole-body st…
How Should a Camera-First AI Agent Work?
Xiaofan Wu, Xi Zeng, Miaoxia Chen +8
Camera-first agents must act on the visual world as it unfolds. We argue that the central obstacle for future camera-first agents is not recognition ability - multimodal models (vi…
SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining
Jicong Ao, Shuhan Jiang, Yuling Zhong +7
The ability to interact with articulated objects is essential for embodied intelligent systems, but collecting large-scale real-world demonstrations for these interactions remains…
NEXUS: Perceptive Whole-Body Control for Terrain-Adaptive Teleoperation
Xiangyu Miao, Junsong Wu, Jiyuan Shi +6
Whole-body teleoperation requires a humanoid robot to reproduce a human operator's behavior even when their terrains differ. This demands that the robot perceive local terrain and…
ReWeight: Leveraging Human Data for VLA Post-Training via Demonstration Retrieval and Sample Weighting
Chenwei Wang, Dianye Huang, Match W. L. Ko +2
Post-training vision-language-action (VLA) models for specific robots and tasks requires in-domain demonstrations, yet collecting diverse robot data is costly. Egocentric human dem…
UMI-Bench 1.0: An Open and Reproducible Real-World Benchmark for Tabletop Robotic Manipulation with UMI Data
Shi Jin, Yuntian Wang, Yuhui Duan +16
Real-robot evaluation is essential for understanding whether learned manipulation policies can operate reliably outside curated demonstrations. This need is particularly pressing f…
SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
Yucheng Deng, Pingrui Lai, Xinhai Li +5
Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instruct…
OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
Zehao Yu, Jiakun Zheng, Weiji Xie +4
Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data source…
VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training
Siyuan Yang, Linzheng Guo, Ouyang Lu +10
Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Visio…
GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation
Xinhai Li, Xiaotao Zhang, Yuehao Huang +10
Embodied navigation connects intelligent agents with the physical world and is fundamental for general robotic intelligence. Limited availability and quality of navigation data hav…
DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation
Sunyao Zhou, Yunzi Wu, Tianhang Wang +5
Long-horizon collaborative vision-language navigation (VLN) is critical for multi-robot systems to accomplish complex tasks beyond the capability of a single agent. CoNavBench take…
AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly
Zhi Jing, Jinbin Qiao, Ouyang Lu +5
Spatial reasoning is a fundamental capability for embodied intelligence, especially for fine-grained manipulation tasks such as robotic assembly. Recent methods based on vision-lan…
PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning
Huanyu Li, Dewei Wang, Xinmiao Wang +4
Humanoid robots often need to balance competing objectives, such as maximizing speed while minimizing energy consumption. While current reinforcement learning (RL) methods can mast…
Beyond Short-Horizon: VQ-Memory for Robust Long-Horizon Manipulation in Non-Markovian Simulation Benchmarks
Honghui Wang, Zhi Jing, Jicong Ao +4
The high cost of collecting real-robot data has made robotic simulation a scalable platform for both evaluation and data generation. Yet most existing benchmarks concentrate on sim…
HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation
Xingyi Wang, Chenyun Zhang, Weiji Xie +4
Humanoid robots deployed in real-world scenarios often need to carry unknown payloads, which introduce significant mismatch and degrade the effectiveness of simulation-to-reality r…
InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
Dayang Liang, Yuhang Lin, Xinzhe Liu +3
Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicabili…
X-Loco: Towards Generalist Humanoid Locomotion Control via Synergetic Policy Distillation
Dewei Wang, Xinmiao Wang, Chenyun Zhang +4
While recent advances have demonstrated strong performance in individual humanoid skills such as upright locomotion, fall recovery and whole-body coordination, learning a single po…
Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction
Yuhang Lin, Jiyuan Shi, Dewei Wang +4
Executing reliable Humanoid-Object Interaction (HOI) tasks for humanoid robots is hindered by the lack of generalized control interfaces and robust closed-loop perception mechanism…
TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control
Weiji Xie, Jiakun Zheng, Jinrui Han +4
Recent advances in humanoid whole-body motion tracking have enabled the execution of diverse and highly coordinated motions on real hardware. However, existing controllers are comm…
Learning Soccer Skills for Humanoid Robots: A Progressive Perception-Action Framework
Jipeng Kong, Xinzhe Liu, Yuhang Lin +4
Soccer presents a significant challenge for humanoid robots, demanding tightly integrated perception-action capabilities for tasks like perception-guided kicking and whole-body bal…
HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control
Jinrui Han, Dewei Wang, Chenyun Zhang +4
While current humanoid whole-body control frameworks predominantly rely on the static environment assumptions, addressing tasks characterized by high dynamism and complex interacti…
Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
Yingnan Zhao, Xinmiao Wang, Dewei Wang +5
Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominant…
Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin, Chenjia Bai, Zihao Zhang +4
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate gr…