7 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
CEER2: Directional and Tunable End-Effector and Root Compliance for Humanoid Loco-Manipulation
Xinyuan Luo, Chunyuan Yang, Boyuan Chen +1
Humanoids are increasingly capable of tracking complex whole-body motions, but physical interaction introduces a different challenge. When a robot makes contact with a person or th…
FRAMES: Failure Recovery And Monitoring of Embodied Skills for Humanoid Loco-Manipulation
Ajay Vikram Periasami, Xinyuan Luo, Haoyu Li +1
Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does not guarantee successful physi…
Robustness of Robotic Manipulation: Foundations and Frontiers
Yifei Dong, Zhanyi Sun, Lujie Yang +5
Humans and animals exhibit remarkable robustness in physical manipulation, yet robots remain far behind. Progress toward human-level manipulation robustness is hindered by the abse…
PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning
Haoyu Li, Aaron Thomas, Shuyan Zhou +1
To perform a wide range of daily tasks, robots need to construct a 3D representation that is semantically rich, physically grounded, and structured enough to support task planning…
Any-ttach: Quick End-effector Swapping Enables Manipulation Dexterity with Simplicity
Weizhe Ni, Jinzhou Li, Haoyu Li +3
Robotic manipulation dexterity is often pursued by building increasingly complex high-DoF multifingered hands. While many robotic hands are designed to replicate human morphology,…
CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation
Xinyuan Luo, Xingrui Chen, Xunjian Yin +6
Humanoid robots have achieved impressive locomotion performance, yet contact-rich and long-horizon manipulation remains a major bottleneck. Manipulation is inherently contact-rich…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…