From the 1 of 13 linked papers with an AI index.
13 papers
Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations
Jialiang Li, Yuhan Wang, Haojun Li +5
The paper introduces HERO, a hierarchical embodied robot agent that autonomously learns manipulation skills from scratch without human demonstrations by bootstrapping experience, r…
Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data
Jialiang Li, Yi Qiao, Yunhan Guo +2
Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. How…
HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
Zehui Zhao, Yuxuan Zhao, Gaojing Zhang +3
Human demonstrations, which can be collected at scale and naturally capture active hand-eye coordination, are a promising data source for learning humanoid loco-manipulation. Howev…
FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
Haotian He, Zeyu Yan, Qipeng Liu +2
Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without ful…
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
Zhongxi Chen, Yifan Han, Yanming Shao +5
Vision-Language-Action (VLA) models have emerged as a promising paradigm for grounding visual-language understanding into real-world robotic manipulation. However, dexterous manipu…
BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances
Yifan Han, Jianxiang Liu, Haoyu Zhang +3
Learning robot manipulation from human videos is appealing due to the scale and diversity of human demonstrations, but transferring such demonstrations to executable robot behavior…