8 papers
CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulation
Yushan Liu, Peibo Sun, Xintao Chao +8
Vision-language-action (VLA) policies commonly execute long-horizon mobile manipulation through open-loop action chunks, issuing multiple actions without receiving new high-level v…
ForgeDrive: Bidirectional Cross-Conditioning for Unified Visual-Action Generation in Autonomous Driving
Xuchang Zhong, He Zheng, Chenxu Zhao +9
World-model-based autonomous driving endows the model with the ability to understand scene evolution. Yet this promise is undermined by the prevailing imagine-then-act paradigm, wh…
PerceptDrive: Perception Prior World-Action Modeling with Adaptive Expert Routing for End-to-End Autonomous Driving
Yushan Liu, Tianxiong Lv, Bohua Wang +11
Frozen perception foundation models encode rich geometric, semantic, and dynamic knowledge. Yet narrow conditioning interfaces may attenuate task-relevant cues, while static fusion…
Self-Supervised Mask-Aware Transformers for Fault-Tolerant FBG Force Sensing in Minimally Invasive Surgical Robotics
Peibo Sun, Shiyuan Dong, Shucheng Ye +5
In minimally invasive surgical robotics, catheter-scale Fiber Bragg Grating (FBG) sensors are promising due to their ability to estimate multi-dimensional forces by multiplexing se…
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
Yushan Liu, Peibo Sun, Shoujie Li +7
World Action Models (WAMs) enhance Vision-Language-Action policies by jointly predicting scene evolution and robot actions, but existing methods usually represent the predicted wor…
PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation
Jingjing Fan, Yushan Liu, Shoujie Li +6
Robot manipulation uses temporal context to select actions and visual foresight to assess their consequences, yet dense representations of past and future observations incur substa…
Exo-ViHa: A Cross-Platform Exoskeleton System with Visual and Haptic Feedback for Efficient Dexterous Skill Learning
Xintao Chao, Shilong Mu, Yushan Liu +4
Imitation learning has emerged as a powerful paradigm for robot skills learning. However, traditional data collection systems for dexterous manipulation face challenges, including…
AVR: Active Vision-Driven Precise Robot Manipulation with Viewpoint and Focal Length Optimization
Yushan Liu, Shilong Mu, Xintao Chao +7
Robotic manipulation in complex scenes demands precise perception of task-relevant details, yet fixed or suboptimal viewpoints often impair fine-grained perception and induce occlu…