Abstract
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.
Abstract (translated)
机器人的普及使得优化设计以改善性能和效率变得越来越重要。然而，当前机器人设计实践忽视了感知和设计选择对机器人学习能力的影响。为了解决这一差距，我们提出了一种综合方法，考虑了机器人感知、硬件特性和任务要求之间的交互作用。我们的算法优化了机器人的整体形态，从而提高了学习和任务执行的 proficiency。为了实现这一点，我们引入了形态学适应控制器(Magic)，该控制器可以帮助快速评估不同机器人设计。 Magic 策略通过一种新颖的基于隐式对齐(PRISM)框架的单一阶段学习方法进行高效训练，同时也鼓励机器人内部观察的典型行为。我们的模拟结果显示，整体优化机器人形态可以在各种操纵任务中提高性能 by 15-20%，而只需要比人类专家形态表现所需的数据少25倍。总之，我们的工作为机器人领域的基于学习的方法趋势做出了贡献，并强调了设计机器人以促进更好学习的潜力。
URL
https://arxiv.org/abs/2303.13390
