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Journal Article

On Designing a Learning Robot: Improving Morphology for Enhanced Task Performance and Learning

As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design prac- tices 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 charac- teristics, 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. The project’s website can be found at


Maks Sorokin
Chuyuan Fu
Jie Tan
C. Karen Liu
Yunfei Bai
Wenhao Yu
Sehoon Ha
Mohi Khansari
Journal Name
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
Publication Date
October, 2023