arXiv 2610.09455 · cs.CV · 2026-10-07
RLHND: Video Foundation Models as Physically Grounded Hand Trackers for Robot Learning
Seungjun Moon, Subin Jeon, Sangwoo Kim, Hanbyul Joo, Jinwoo Shin
RLWRLD
在最新榜 #260 本站收录于 2026-10-21
一句话要点
RLHND 基于视频基础模型从第一视角视频估计手部姿态和触觉信息，让人类视频更好地用于机器人学习。
RLHND uses a video foundation model to estimate hand pose and touch from egocentric video, making human videos more useful for robots.
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主题与学科
同主题：机器人与具身
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- In-Context Learning for Robots: Methods and Applications ▲388
- HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents ▲313
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives ▲310
- Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience ▲179
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
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