10 papers
Spline Policy: A Structured Representation for Robot Policies
Mengze Tian, Yiming Li, Sichao Liu +2
Modern imitation-learning policies for robot manipulation often represent actions as fixed-resolution action chunks, which are simple and effective but expose limited geometric and…
Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation
Xiangyi Zheng, Xiangyu Wang, Qinan Liao +6
Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navi…
World Models for Robotic Manipulation: A Survey
Fangyuan Wang, Ziyuan Wang, Guorui Pei +15
Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world models provide this capability b…
Integrated Exploration and Sequential Manipulation on Scene Graph with LLM-based Situated Replanning
Heqing Yang, Ziyuan Jiao, Shu Wang +3
In partially known environments, robots must combine exploration to gather information with task planning for efficient execution. To address this challenge, we propose EPoG, an Ex…
RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation
Hao Li, Ziqin Wang, Zi-han Ding +9
Advances in large vision-language models (VLMs) have stimulated growing interest in vision-language-action (VLA) systems for robot manipulation. However, existing manipulation data…
ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models
Linqing Zhong, Yi Liu, Yifei Wei +4
Vision-Language-Action models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs i…
Geometry-aware Policy Imitation
Yiming Li, Nael Darwiche, Amirreza Razmjoo +4
We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action…
RAP: 3D Rasterization Augmented End-to-End Planning
Lan Feng, Yang Gao, Eloi Zablocki +5
Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be…
Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges
Shuai Li, Chen Yizhe, Li Dong +4
The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision langu…
Liaohe-CobotMagic-PnP: an Imitation Learning Dataset of Intelligent Robot for Industrial Applications
Chen Yizhe, Wang Qi, Hu Dongxiao +10
In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Ef…