2 citations · 2 across the 19 of their papers we have counts for
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Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents
Guanxiong Chen, Qianjun Xia, Jiawei Peng +21
Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover…
MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
Yuxin Jiang, Chang Yu, Yunuo Chen +4
Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observation window, which renders long-h…
Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Yu Guo, Chang Yu, Siyu Ma +4
Explicit 3D representations are attractive for manipulation because they expose object shape, workspace geometry, and robot-object relations in metric coordinates. However, sparse…
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Siyu Ma, Yuqi Liang, Chang Yu +5
Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state requ…
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
Tianyi Xie, Haotian Zhang, Jinhyung Park +17
Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture a…
MetaWorld-X: Hierarchical World Modeling via VLM-Orchestrated Experts for Humanoid Loco-Manipulation
Yutong Shen, Hangxu Liu, Penghui Liu +6
Learning natural, stable, and compositionally generalizable whole-body control policies for humanoid robots performing simultaneous locomotion and manipulation (loco-manipulation)…