1 citations · 1 across the 7 of their papers we have counts for
9 papers
RecastVLA: From Past Interaction to Future Control with Adaptive Policy States
Wenbo Li, Jun Yang, Yiteng Chen +2
Sequential manipulation requires a robot to track what has already happened, even when the current scene no longer reveals it. Policies with explicit history representations make p…
SCULPT-VLA: Learning Structured Control through Staged Action Grounding
Wenbo Li, Yiteng Chen, Wei Zhang +3
Vision-language-action (VLA) policies increasingly incorporate structured intermediate supervision beyond action labels. Yet specifying what an intermediate representation should e…
LIFD: Anchored Diffusion for 3D-Aware Scene Memory in Robotic Manipulation
Wenbo Li, Yiteng Chen, Wenhao Li +1
During manipulation, robot and scene motion can move previously observed regions outside the camera's field of view. Geometry-aware RGB features encode visible structure, while con…
Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach
Yuli Song, Wenbo Li, Wenci Xin +3
Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments. To extend the reachable workspace, we built a multi-segment fl…
RoboRouter: Training-Free Policy Routing for Robotic Manipulation
Yiteng Chen, Zhe Cao, Hongjia Ren +9
Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA) policies, and code-based composi…
CleanUpBench: Embodied Sweeping and Grasping Benchmark
Wenbo Li, Guanting Chen, Tao Zhao +5
Embodied AI benchmarks have advanced navigation, manipulation, and reasoning, but most target complex humanoid agents or large-scale simulations that are far from real-world deploy…
FrankenBot: Brain-Morphic Modular Orchestration for Robotic Manipulation with Vision-Language Models
Shiyi Wang, Wenbo Li, Yiteng Chen +2
Developing a general robot manipulation system capable of performing a wide range of tasks in complex, dynamic, and unstructured real-world environments has long been a challenging…
T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models
Yiteng Chen, Wenbo Li, Shiyi Wang +2
Building a general robotic manipulation system capable of performing a wide variety of tasks in real-world settings is a challenging task. Vision-Language Models (VLMs) have demons…
AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation
Wenbo Li, Shiyi Wang, Wenhao Li +3
Natural-language instructions specify manipulation goals but leave the robot's motion underdetermined. We present AntiGrounding, a visual action-selection framework built around a…