動画から二足歩行ロボットの制御へ：MotionSkill
MotionSkill: From Bipedal Motion Video to Control across Embodiments
単眼動画から歩行のタイミング付き参照動作を抽出し、体格の異なる10種類の二足ロボットに合わせて適応させ、各ロボットの残差ポリシーを学習することで歩行制御を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Philip Niu, Xiaofeng Han, Sylvie Pan, Yang Wang
分類: cs.RO
原文アブストラクト
Reusing a demonstrated gait can reduce the work of specifying the same motion for each biped. A video captures the timing of a step, but each robot must execute it with its own limbs and actuators. MotionSkill recovers a timed reference once from monocular video and adapts it to each target in simulation. It aligns root motion and body landmarks in a common frame and records which landmarks are available at each instant. At each source timestamp, the mapper preserves limb directions, uses the target segment lengths, and solves inverse kinematics under joint limits and closure constraints. In MuJoCo, we train a separate residual policy for each of ten bipeds on its mapped reference from one four-second walking clip. Each policy uses the measured robot state to correct joint commands during contact. From initial states held out during training, MotionSkill completed 98% of trials on the original video and 26% with the upper body occluded for one second.