From the 1 of 25 linked papers with an AI index.
1 citations · 1 across the 15 of their papers we have counts for
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SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control
Bikang Pan, Fan Liu, Haotao Lu +2
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations. However, conditioning future prediction only on the task prompt and ob…
LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents
Jingya Wang, Yuyang Gao, Liuzhenghao Lv +2
LabEvolver is a training‑free framework that gives wet‑lab robotic agents episodic memory and safety checks by combining an adaptive inner trial loop with an outer evolution loop t…
ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
Yunao Huang, Shiyu Sang, Haotao Lu +5
Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust control. However, scaling visuo-…
TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
Suting Ni, Hanbing Zhang, Zhenyu Wei +4
Tactile feedback is fundamental to Hand-Object Interaction (HOI), governing contact formation, force regulation, and stable manipulation, making it essential for achieving true hum…
PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models
Peizheng Guo, Jingyao Wang, Changwen Zheng +1
Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging, because a manipulation task…
Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
Zhirui Liu, Kaiyang Ji, Ke Yang +4
Enabling humanoid robots to follow free-form natural language commands is a critical step toward seamless human-robot interaction and general-purpose embodied AI. However, existing…