6 papers
ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation
Yuntian Wang, Zesheng Jia, Yuhui Duan +5
Learning visuomotor policies for long-horizon manipulation remains a fundamental challenge. Recent skill-based imitation learning methods based on discrete quantization have shown…
GUIDE: Goal-Initialized Directional Understanding for End-to-End Visual Navigation
Liang Wang, Jin Jin, KanZhong Yao +6
Learning-based visual navigation for legged robots typically relies on continuous goal updates from hierarchical state estimation to provide a persistent directional reference. Thi…
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…
Vision-Language-Policy Model for Dynamic Robot Task Planning
Jin Wang, Kim Tien Ly, Jacques Cloete +3
Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robotics. This requires robots to perceive an…
Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Boyuan Deng, Luca Rossini, Jin Wang +3
Adaptive recovery from fall incidents are essential skills for the practical deployment of wheeled-legged robots, which uniquely combine the agility of legs with the speed of wheel…
INTENTION: Inferring Tendencies of Humanoid Robot Motion Through Interactive Intuition and Grounded VLM
Jin Wang, Weijie Wang, Boyuan Deng +3
Traditional control and planning for robotic manipulation heavily rely on precise physical models and predefined action sequences. While effective in structured environments, such…