SOFTZOO: A SOFT ROBOT CO-DESIGN BENCHMARK FOR LOCOMOTION IN DIVERSE ENVIRONMENTS

Abstract

While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and accelerate the development of new breeds of soft robots, a comprehensive virtual platform -with well-established tasks, environments, and evaluation metricsis needed. In this work, we introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturallyinspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. Further, it provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control. Combined, these elements form a feature-rich platform for analysis and development of soft robot co-design algorithms. We benchmark prevalent representations and co-design algorithms, and shed light on 1) the interplay between environment, morphology, and behavior 2) the importance of design space representations 3) the ambiguity in muscle formation and controller synthesis and 4) the value of differentiable physics. We envision that SoftZoo will serve as a standard platform and template an approach toward the development of novel representations and algorithms for co-designing soft robots' behavioral and morphological intelligence. Demos are available on our project page 1 .

1. INTRODUCTION

The natural world demonstrates morphological and behavioral complexity to a degree unexplored in soft robotics. A jellyfish's gently undulating geometry allows it to efficiently travel across large bodies of water; an ostrich's spring-like feet allow for fast, agile motion over widely varying topography; a chameleon's feet allows for dexterous climbing up trees and across branches. Beyond their comparative lack of diversity, soft robots' designs are rarely computationally optimized in silico for the environments in which they are to be deployed. The degree of morphological intelligence observed in the natural world would be similarly advantageous in artificial life. In this paper, we present SoftZoo, a framework for exploring and benchmarking algorithms for codesigning soft robots in behavior and morphology, with emphasis on locomotion tasks. Unlike pure control or physical design optimization, co-design algorithms co-optimize over a robot's brain and body simultaneously, finding more efficacious solutions that exploit their rich interplay (Ma et al., 2021; Spielberg et al., 2021; Bhatia et al., 2021) . We have seen examples of integrated morphology and behavior in soft manipulation (Puhlmann et al., 2022 ), swimming (Katzschmann et al., 2018 ), flying (Ramezani et al., 2016 ), and dynamic locomotion (Tang et al., 2020) . Each of these robots was designed manually; algorithms that design such robots, and tools for designing the algorithms that design such robots have the potential to accelerate the invention of diverse and capable robots.

funding

* This work was done during an internship at the MIT-IBM Watson AI Lab. Support for this work was also provided in part by the NSF EFRI Program (Grant No. 1830901), DARPA MCS Program, MIT-IBM Watson AI Lab, and gift funding from MERL, Cisco, and Amazon. 1 Project Page: https://sites.google.com/view/softzoo-iclr-2023 1

