7 papers
BiliVLA: Scene-Aware Vision-Language-Action Model with Reinforcement Learning for Autonomous Biliary Endoscopic Navigation
Jinsong Lin, Chi Kit Ng, Zhiyong Xiong +8
Endoscopic retrograde cholangiopancreatography (ERCP) demands precise endoscopic navigation and stable biliary cannulation within a narrow monocular field characterized by specular…
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
Zikang Xiong, Weixin Li, Zhouchonghao Wu +6
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all sta…
OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
Yingdong Hu, Haodong Zhu, Boyuan Zheng +6
Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into se…
Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
Ruiqian Nai, Boyuan Zheng, Junming Zhao +8
Current approaches for humanoid whole-body manipulation, primarily relying on teleoperation or visual sim-to-real reinforcement learning, are hindered by hardware logistics and com…
TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance
Yuyang Liu, Chuan Wen, Yihang Hu +2
Designing dense rewards is crucial for reinforcement learning (RL), yet in robotics it often demands extensive manual effort and lacks scalability. One promising solution is to vie…
Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials
Yihang Hu, Pingyue Sheng, Yuyang Liu +2
Embodied robots have achieved strong performance in many real-world manipulation tasks, yet agile dynamic manipulation remains challenging due to high sensitivity to motion paramet…
SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation
Shengjie Wang, Jiacheng You, Yihang Hu +2
Real-world tasks such as garment manipulation and table rearrangement demand robots to perform generalizable, highly precise, and long-horizon actions. Although imitation learning…