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.
翻译:随着机器人变得越来越普遍,优化其设计以提高性能和效率变得越来越重要。然而,当前的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。为了填补这一空白,我们提出了一种全面的方法,考虑了机器人的感知、硬件特性和任务要求之间的相互作用。我们的方法全面优化机器人的形态,以提高其学习和任务执行能力。为了实现这一目标,我们引入了一种形态不可知的控制器(MAGIC),该控制器有助于快速评估不同的机器人设计。MAGIC 策略经过一个新颖的受特权的单阶段学习框架培训,该框架还鼓励典型的机载观测行为。我们基于模拟的结果表明,全面优化的形态可以将机器人性能在不同的操作任务中提高15-20%,并且需要 25 倍的数据才能匹配人类专家制作的形态表现。总之,我们的工作为机器人领域日益增长的基于学习的方法做出了贡献,并强调了设计促进更好学习的机器人的潜力。