両側性制約で符号化された行動の回復
Recovery of Behaviors Encoded via Bilateral Constraints
望ましい行動を階層的な微分制約として記述し、符号化テンプレートを通すことで、損傷や環境変化後でも数十回の試行で高自由度の運動行動を迅速に再学習・回復できる手法を提案した。
著者: George Council, Shai Revzen
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
If robots are ever to achieve autonomous motion comparable to that exhibited by animals, they must acquire the ability to quickly recover motor behaviors when damage, malfunction, or environmental conditions compromise their ability to move effectively. We present an approach which allowed our robots and simulated robots to recover high-degree of freedom motor behaviors within a few dozen attempts. Our approach employs a behavior specification expressing the desired behaviors in terms as rank ordered differential constraints. We show how factoring these constraints through an encoding template produces a recipe for generalizing a previously optimized behavior to new circumstances in a form amenable to rapid learning. We further illustrate that adequate constraints are generically easy to determine in data-driven contexts. As illustration, we demonstrate our recovery approach on a physical 7 DOF hexapod robot, as well as a simulation of a 6 DOF 2D kinematic mechanism. In both cases we recovered a behavior functionally indistinguishable from the previously optimized motion.
