学習ロボットの設計：タスク性能と学習効率を高める形態最適化
On Designing a Learning Robot: Improving Morphology for Enhanced Task Performance and Learning
知覚・ハードウェア・タスク要件の相互作用を考慮し、ロボットの形態を包括的に最適化する手法を提案。MAGICコントローラとPRISM学習フレームワークにより、様々なマニピュレーションタスクで性能が15〜20%向上し、人間設計の形態と同等の性能を25倍少ないデータで達成。
著者: Maks Sorokin, Chuyuan Fu, Jie Tan, C. Karen Liu, Yunfei Bai, Wenlong Lu, Sehoon Ha, Mohi Khansari
分類: cs.RO
原文アブストラクト
As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design practices overlook the impact of perception and design choices on a robot's learning capabilities. To address this gap, we propose a comprehensive methodology that accounts for the interplay between the robot's perception, hardware characteristics, and task requirements. Our approach optimizes the robot's morphology holistically, leading to improved learning and task execution proficiency. To achieve this, we introduce a Morphology-AGnostIc Controller (MAGIC), which helps with the rapid assessment of different robot designs. The MAGIC policy is efficiently trained through a novel PRIvileged Single-stage learning via latent alignMent (PRISM) framework, which also encourages behaviors that are typical of robot onboard observation. Our simulation-based results demonstrate that morphologies optimized holistically improve the robot performance by 15-20% on various manipulation tasks, and require 25x less data to match human-expert made morphology performance. In summary, our work contributes to the growing trend of learning-based approaches in robotics and emphasizes the potential in designing robots that facilitate better learning.
