Problem
Research questions and friction points this paper is trying to address.
depth-only
end-to-end
humanoid soccer skills
DAVIS: A Depth-Only End-to-End Active-Vision Framework for Humanoid Soccer Skills
🤖 AI Summary
研究提出DAVIS框架，仅使用深度图像和本体感受历史，直接输出关节PD目标，解决机器人足球技能学习中的感知、接近、对齐等问题。
📝 Abstract
Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alignment, impact, and recovery while its own motion induces substantial viewpoint changes, frequent loss of the ball from view, and uncertain contact outcomes. In this work, we ask a compact yet stricter question: can a humanoid learn soccer contact skills using only a head-mounted depth image, proprioceptive history, and an optional low-dimensional task command, and directly output 25-DoF joint PD targets without extra runtime perception or planning modules? To this end, we propose DAVIS, a depth-only end-to-end framework for humanoid soccer skills that learns visibility-aware auxiliary geometry during training, and combines GT-to-prediction annealing, task curricula, and AMP-style motion priors to smoothly bridge privileged supervision and real deployment. Built on this framework, we instantiate representative soccer contact skills, including goal-directed shooting and directional dribbling, through task-specific definitions of objects, commands, rewards, and curricula, and validate them through simulation, Noetix E1 real-robot experiments, and ablations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
depth-only
end-to-end framework
visibility-aware
GT-to-prediction annealing
task curricula
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