Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Authors: Jagdeep Singh Bhatia
Holly Jackson
Yunsheng Tian
Jie Xu
Wojciech Matusik
Venue: NeurIPS 2021 Poster (2021)
Keywords: Co-design
Benchmark
Soft Robotics
Evolutionary Algorithms
Reinforcement Learning
Machine Learning

Abstract: Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e.g., soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http://evogym.csail.mit.edu/.

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Jagdeep Singh Bhatia
Holly Jackson MIT CSAIL
Yunsheng Tian MIT CSAIL
MIT CSAIL j jagdeep@mit.co edu
hjackson@mit..co edu
yunsheng@csail mit. edu
Jie Xu
Wojciech Matusik
MIT CSAIL
MIT CSAIL
jiex0csail wwt. edu
wojciechhceail, mit. edu
Abstract
Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learn- ing and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in roboties is characterized as challenging problem, and more importantly, comprehensive evaluation benchmark for co-optimization does exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. types of voxels (e.g., soft, rigid, actuators), resulting in modular and expressive robot design space. Our benchmark environments span wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Eval- uating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors evolution progresses, with the best evolved designs solving many our proposed tasks. Additionally, even though robot de- signs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in hardest environments. This suggests that more advanced algorithms are required explore the high-dimensional design space and evolve increasingly intelligent robots an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http: / //v/g//vv.mmm///////o//////..... csail. mit edu.
our benchmark. each robot is composed of different
Introduction
One of the main goals of artificial intelligence to develop effective approaches for the creation of embodied intelligent systems. Inspired from real organisms, where body structure and brain are two key factors for completing any task real environment, a successful intelligent robot typically requires concurrently optimizing its structure design and control mechanism. Such co-design problem has been a long-standing key challenge in the robotics and machine learning communitics. Surprisingly, despite its importance, most previous research works still either only develop complex control algorithms for existing robot structures [1, 2, 17, 30], or conduct co-optimization over robot morphology and control for only few simple tasks (e.g., running, jumping) [7, 14, 31, 32), especially
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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in the soft body domain. The primary reasons behind the under- exdloreeploottion of co-design algorithms in sophisticated problems are: (1) the underlying complex bilevel optimization scheme of a co-design algorithm, where the inner control optimization loop leads to long iteration cycle of the whole optimization process; (2) the lack of well-established benchmark platform providing the researchers with suite to evaluate and compare different algorithms.
Digital benchmark environments have proven be successful at promoting the development of ad- vanced learning techniques via providing a comprehensive evaluation suite to make fair comparisons among different algorithms [5. 11, 36). However, to our best knowledge, all existing benchmark plat- forms constrain their domains within control optimization problems, and the space of co-optimization environment suites is still rarely explored.
To fill this gap, in this work we propose Evolution Gym, a large-scale benchmark for evolving both the shape structure and controller of soft robots. The body of each robot in Evolution Gym is composed of various types of primitive building blocks (e.g., soft voxels, rigid voxels, actuator voxels), and the control of the robot includes action signals applied on the actuator voxels. We choose to use this multi-material voxel-based structure as the representation of robot body since it provides a general and universal representation for various categories of robot designs, and at the same time results in a modular and expressive structure design space. We adopt a mass-spring dynamics system [26] with penalty- -based frictional contact as the underlining physies engine. Such light-weight simulator allows the co-design algorithms to significantly reduce the simulation cost and thus accelerate the develop-evaluae evaluate iteration cycle [3, 15, 23]. The back-end simulator is fully developed in C++ to provide further computing efficiency. Another feature of Evolution Gym is its large variety of tasks categorized by varying difficulty levels, which offer an extensive evaluation benchmark for comparing approaches. The benchmark is currently comprised of more than 30 tasks, spanning locomotion on various types of terrains and manipulation. Moreover, Evolution Gym is easy to use. In order to have user-friendly interfaces, we build a Python wrapper outside the C++ simulator and carefully design our APIs off of the well-received APIs of OpenAl Gym with minimum modifications. Evolution Gym will be released fully open-source under the MIT license.
In addition, we develop several baseline algorithms by integrating state-of of-the-art design optimization approaches and reinforcement learning techniques. Specifically, in our baseline algorithms, design optimization methods are served in the outer loop to evolve the physical structures of robots and reinforcement learning algorithms are applied in the inner loop to optimize controller for given proposed structure design. We conduct extensive experiments to evaluate all baseline algorithms on Evolution Gym. The experiment results demonstrate that intelligent robot designs can be evolved fully autonomously while outperforming hand-designed robots in easier tasks, which reaffirms the necessity of jointly optimizing for both robot structure and control. However, none of the baseline algorithms are capable enough to successfully find robots that complete the task in our hardest environments. Such insufficiency of the existing algorithms suggests the demand for more advanced robot co-design techniques, and we believe our proposed Evolution Gym provides a comprehensive evaluation testbed for robot co-design and unlocks future research in this direction.
In summary, our work has the following key contributions: (i) We propose Evolution Gym, the first large-scale benchmark for soft robot co-design algorithms. (ii) We develop several co-design algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques for control optimization. (iii) The developed algorithms are evaluated and analyzed on our proposed benchmark suite, and the results validate the efficacy of robot co-design while pointing out the failure and limitations of existing algorithms.
2
Related work
Robot co-design Co-designing the structure (i.e., body) and control (i.e., brain) of robots is long-standing key challenge in the robotics community. As the earliest work in this space, Sims [31] represents the structure of rigid robot as directed graph and proposes an evolutionary algorithm defined on graphs to optimize the robot design. Subsequently, the co-design of rigid robots formulated as a graph search problem where more efficient search algorithms are applied [13, 27, 39, 41] to achieve increasingly interesting results. However, with the restriction of having rigid components only, these algorithms are unable to produce optimal or even feasible designs for many challenging tasks where a compliant joint or robot component is required to achieve the goal.

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On the contrary, soft components offer much more flexibility to represent arbitrary shapes, making the design of more complex, agile, and high-perttee performirn robots possible. Inspired by this, some work has been conducted to co-design robots composed of soft cells. Cheney et al. [7, 8]; Van Diepen and Shea [37]; Corucci et al. [10] propose evolutionary algorithms co-optimize the structure and control of voxel-based robots. However those algorithms typically parameterize the control as an open-loop periodic sequence of actuation, which prevents robots from learning complex non-periodic tasks such walking uneven or varying terrains. Spielberg et al. [32] and Medvet et al. [23] jointly optimize the spatial-varying material parameters and the neural network policy for soft robots but leave the shape of the robot fixed. Our proposed benchmark shares similar expressive structure design space as Cheney et al. [7], but allows the control to be parameterized by a sophisticated neural network feedback policy. To handle such sophisticated joint optimization of the robot structure and high-dimensional neural network control policy, we develop several baseline co-design algorithms by combining state-of-the-ar design optimization strategies and reinforcement learning techniques for control optmization.
Benchmark environments for robotics learning Present research in robotics learning is largely facilitated by emerging benchmark environments. For instance, OpenAl Gym [5], DeepMind Control Suite [36), rllab [11], and Gibson [40] have been developed to benchmark RL algorithms for controlling rigid robots. At the same time, PlasticineLab [16] is specifically designed for soft robot learning. However, the existing benchmark environments only. To enable the possibility of evolving the structure robot, the existing co-design work has to either implement their own testing environment [32, 7, 8. 10, 37J. make substantial changes the underlying code of the existing control-only environments [29]. The independent development of testing beds requires non-trivial workload, and as result, existing co-design works mainly focus on evaluating the robot on a few simple tasks such as walking on a flat terrain [7, 6, 8, 37, 32, 23), or swimming along a single direction [9, 39J. An unintended consequence of such independency is an indirect comparison among different algorithms. Evolution Gym fills this gap by presenting large variety of tasks with different difficulty levels that span from locomotion manipulation. The proposed benchmark suite can be effectively used to test the generalizability of the algorithms on different tasks, potentially accelerating research in robot co-design.
all constructed for learning the control
3 Evolution Gym
Evolution Gym
Co-Design Algorithm
Back-End Soft Body Simulator
Design Optimization
Actuator
New Design
Task Specific Environments
Observation
Reward
Control Optimization
Action
Design
Evolution Gym
Figure I: Overview of Evolution Gym and its integration with the co-design algorithms. Evolu- tion Gym comprised of a back-end soft body simulator (A, B) and task-specific environments (C). user-customized co-design algorithm can be plugged to optimize for both robot structure and control through interacting with Evolution Gym certain task.
3.1 Overview
In this section, we present Evolution Gym, a large-scale benchmark for the co-design of voxel-based soft robots. Evolution Gym is featured by its versatile and expressive multi-material voxel-based structure design space, flexibility of the controller parameterization, wide spectrum of tasks of various difficulty levels, fast back-end soft-body simulation support, and user-friendly Python interfaces.

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As shown in the overview in Figure 1, Evolution Gym is comprised of a task-specific environment and back-end soft-body simulator. The gym suite provides seamless interfaces with a user-defined co-design algorithm. The co-design algorithm typically consists of a design optimizer and a control optimizer. The design optimizer can propose new robot structure to the control optimizer, then the control optimizer will compute an optimized controller for the given structure through interactions with Evolution Gym and finally return the maximum reward that this robot structure can achieve. In this way, Evolution Gym provides an easy-to-use platform for co-design algorithms to evolve both robot structure and control to optimize for robots' task performances. Evolution Gym is designed to be the first comprehensive testbed for benchmarking and comparing different co-design algorithms with the hope to facilitate the development of more novel and powerful algorithms in the co-design field.
3.2 Multi-material voxel-based representation
Evolution Gym employs unified multi-materinn voxel-based representation for all the components in the environment (e.g., robot, terrain, object) as shown in Figure 1A. Specifically, each robot in our gym is composed of rigid voxels, soft voxels, horizontal/vertical actuator voxels, and empty voxels. For terrain and objects, we use the same voxel-based structure but with passive voxel types (i.e,, soft/rigid voxels).
We chose a voxel-based representation for three main reasons. First, such multi- ciltiiimtteraal structure of robots provides a general and universal representation for various categories of robot designs and results in a modular structure design space. Additionally, with just the few voxel types described above, and less than 100 voxels per robot, we are able to construct a wide diversity of morphologies due the resulting combinatorial robot design space. Even with this simple representation. our designed robots are capable of performing complex motions and completing difficult tasks. Finally, voxel-based robots can be simulated by fast mass-spring simulation (see section 3.4) which allows our framework to be efficient enough to train robots in matter of minutes and provides a computationally tractable benchmark for iterating co-design algorithms.
3.3
Task representation
Each task in Evolution Gym contains robot structure proposed by the co-design algorithm, environ- ment specifications (e.g.. terrain, object), and a task-related goal (e.g., locomotion or manipulation). The tasks interface with the co-design algorithm through a few key elements including robot structure specification, observation, action, and reward. We introduce each element in detail below.
Robot structure specification As described in Section 3.2, we construct each robot from primitive building blocks arranged on a grid layout. In code, each robot is specified as a material matrix of voxels M and a connection link list C. The value of entry type from the set (Empty, Rigid, Soft, Horizontal Actuator, Vertical Actuator). The connection link list C stores list of connection pairs of adjacent voxels. The co-design algorithm robot structure in the environment through initialization function with M and C as arguments.
.M is a label corresponding to a voxel
update the
The observation is composed in each step to inform the controller of state information of the robot, terrain information of the environment, and goal-relevant information. More specifically, let N be the total number of voxel corner points of the robot. Then the state information of the robot in our tasks is a (2NV + 3-D vector including the relative position of each voxel corner with respect to the center of mass of the robot (2N-D), and the velocity and orientation of center of mass (3-D). To handle complex tasks, specifically those with varying terrain types, an additional observation vector including terrain information is provided. We compile terrain information within a local window of size 2W around the robot into a length-22 22W vector observation that describes the terrain's 's clevation. Furthermore, goal-related information is offered to inform the controller of the execution status of the current task. This goal-related observation is task-specific and is defined on each task separately. For instance, in manipulation tasks where the robot interacts with some object O provide orientation and velocity as well as the position of O's center of mass relative to the robot.
Observation
Action At each time step, an action vector from the robot's controller is provided to step Evolution Gym's simulator. In Evolution Gym, each component of the action vector is associated with an actuator voxel (either horizontal or vertical) of the robot, and instructs deformation target of that

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Locomotion Tasks
Object Manipulation Tasks
Walker [Easy)
(Easy)
Catcher (Hard)
Bridge Walker (Easy)
Thrower (Medium)
Climber (ottduum
Up Stepper (Medium)
Beam Slider (Hard)
Lifter (Hard)
Traverser (Hard)
Figure visual overview selected 10 environments from Evolution Gym. A verbal descrip- tion of tasks is provided in Section 3.5.
voxel. Specifically, the action value within the range [0.6, 1.6], and corresponds to a gradual expansion/contraation of that actuator to u times its rest length.
Reward Each task is equipped with reward function measuring the performance of the current robot and the control action. The value of the reward is defined step-wise and fed back to the agent through step function. The reward function highly task-specific and should be defined to precisely characterize the robot's s completeness of the task. Please refer to Section 3.5 and Appendix for detailed descriptions of the reward functions on each task.
3.4 Simulation engine
We model the dynamics of the underlying simulator as 2D mass-spring system [26]. This simple, flexible formulation allows us to efficiently model soft robots with a wide range of capabilities in wide range of environments. The simulation engine is written entirely in C++. We create Python bindings of our simulator so seamlessly interfaces with standard learning frameworks.
The simulation represents objects and their environment as a mass-spring system in grid-like layout (Figure IB). Objects and their environments are initialized voxels. On initialization, each voxel is a cross cross-braced square, but may undergo deformation as the simulation progresses. Each edge acts as an ideal spring obeying Hooke's 's law, with spring constant defined by one of five possible material types. We employ symplectic RK-4 integration to step forward the simulation.
of non-overlapping, connected
Collision detection performed using a bounding-box tree structure [12]. Penalty-based contact forces and frictional forces computed proportionally to the depth of penetration of the correspond- ing voxels in contact, and are applied on the voxel vertices in the normal and tangential directions of the contact respectively. Please refer Appendix A for more details of simulation.
3.5 Benchmark environment suite
We have developed over 30 unique tasks with Evolution Gym and select tasks here to illustrate the diversity and comprehensiveness of our benchmark task set. All tasks are organized into two categories locomotion and manipulation though some tasks are mix of both. We further classify the tasks into different difficulty levels (i.e., easy, medium, hard) based on the performance of the baseline algorithms (see Section on them. We briefly introduce the selected tasks in this section. For more detailed descriptions and visualizations of the tasks, please refer to our website or Appendix B. It is also worth mentioning that our gym designed be extendable and the user can easily create new tasks for their needs.
3.5.1 Locomotion tasks
Walker (Fasy) This a common standard task typically considered by previous works where the robot needs to walk on flat terrain as fast as possible.

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Bridge Walker (Easy) In this task, the robot traverses series of soft "rope" bridges separated by fixed pillars, and similarly as before needs maximize forward speed.
Up Stepper (Medium) The agent walks up fixed staircase with steps of varying length.
Climber (Medium) The robot must climb two tall fixed walls on each side. The robot is rewarded by its upward climbing speed.
Traverser (Hard) In this hard task, the robot needs to traverse a pit of rigid blocks to get to the other side without sinking into the pit.
3.5.2 Object manipulation tasks
Carrier (Easy) The robot needs to catch small, soft rectangular object initially dropped from above and then carry it along the forward direction. The robot rewarded by the distance both it and the object have traveled.
Thrower (Medium) The rohot throws soft rectangular box far possible without moving itself significantly from its original position.
Beam Slider (Hard) In this task, beam sits on top of a set of spaced-out floating platforms. The robot is rewarded for moving to the beam and sliding the forward direction.
Catcher (Hard) The agent needs to catch a spinning object randomly falling from a high location.
Lifter (Hard) The robot has manipulate an object and lift it out of hole.
4 Evolving soft robots
Robot evolution/co-destt algorithms are formulated as two-level optimization problem, which involves a design optimization method that evolves physical structures of the robots in the outer loop and control optimization algorithm that computes an optimized controller for a given robot structure the inner loop, as illustrated Algorithm 1. We briefly introduce several instantiations of design optimization methods and control optimization methods in Section 4.1 and 4.2 that we use for evaluation on our benchmark, and more details can be found in Appendix C.
Algorithm 1 Algorithmic framework of robot evolution
Inputs: Task specification T, number generations n, population size p.
Outputs: The best robot design D" and controller C*. S+
// Dataset of robot designs, controllers and reward // Sample initial population of robot designs
D1. ... Dp +- SAMPLEDESIGNS(p))
for i <- to n do
for
1 to p do
Cj <- OPTIMIZECONTROLA Dj) // Optimize the controller of given robot design r. EVALUATEREWARD([? Dú. Cj) Evaluate the reward of given design and controller // Update the evaluation result to the dataset
S + S S ((D,,,, r,,r,)} Di,..., D, + OPTIMIZEDESICNS(S, p) // Optimize a population of robot designs to evaluate Find the best design D and controller C* in dataset S with the maximum reward r".
4.1 Design optimization
Design optimization aims at evolving robot structures to maximize the reward under two physical constraints: the body has to be connected, and actuators must exist. In this section, we introduce three instantiations of the design optimization algorithm (OPTIMIZEDESIGN in Algorithm 1).
Genetic algorithm (GA) GAs [24] are widely used in optimizing black-box functions by relying on biologically inspired operators such as mutation, crossover and selection, demonstrated in previous works on evolving rigid robots [31, 39]. We implement simple GA using elitism selection and a each generation,
simple mutation strategy to evolve the population of robot designs. Specifically, our elitism selection works by keeping the top x% of the robots from the current population as survivors and discarding the rest, where x decreases gradually from 60 to 0 over generations. Next, we iteratively sample and mutate one of those survivors with 10% probability of changing each voxel

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of the robot to create more offssringss Note that by mutating voxel type from/to empty voxel, we are able to change the topology of the robot. The crossover operator not implemented in our genetic algorithm.
Bayesian optimization (BO) BO (20, 25] is commonly used global optimization method for black-box functions by learning and utilizing a surrogate model, which is usually employed to optimize expensive-t--evaluate functions, including evolving rigid robots in previous works [29, 21]. Specifically, we choose a batch BO algorithm as described in Kandasamy et al. [18] and implemented in the GPyOpt package [4] that supports categorical input data. We use Gaussian processes as the surrogate model, batch Thompson sampling for extracting the acquisition function, and L-BFGS algorithm to optimize the acquisition function. To ensure fair comparison with other population- based evolutionary baseline algorithms, the batch size of this algorithm is set equal to the population size of other algorithms.
CPPN-NEAT CPPN-NEAT is the predominant method for evolving soft robot design in previous literature [6. 7, 8J. In this method, the robot design parameterized by a Compositional Pattern Producing Network (CPPN) [33]. The input to a CPPN is the spatial coordinate of a robot voxel and the output is the type of that voxel. Therefore, by querying the CPPN at all the spatial locations of a robot, we can obtain the type for each voxel to construct a robot. At the same time the NeuroEvolution of Augmenting Topologies (NEAT) algorithm [34] is used to evolve the structure of CPPNs by working as genetic algorithm with specific mutation, crossover, and selection operators defined on network structures. Our implementation of CPPN-NEAT based on the PyTorch-NEAT library [28) and the neat-python library [22].
4.2 Control optimization
In this section, we introduce the specific control optimization algorithm (OPTIMIZECONTRO) in Algorithm 1) that we use in the robot evolution algorithms. In previous works on evolving soft robots, the controller either encoded as fixed periodic sequence of actuation [7] or is parameterized as CPPN that outputs the frequency and phase offset of the periodic actuation for each voxel [8]. However, the periodic pattern of the control prevents robots from learning complex non-periodic tasks such as walking uneven varying terrains. Therefore, [35] to train the controller, making it possible for the soft robots to perform arbitrarily complex tasks in our benchmark. Specifically, we apply tfate-of-the-art RL algorithm named Proximal Policy Optimization (PPO) [30] for control optimization of robots, with code implementation given by [19].
use reinforcement learning (RL)
Experiments and results
In this section we present the evaluation results of baseline robot co-design algorithms on 10 selected benchmark tasks described in Section 3.5. The complete evaluation results on all our benchmark tasks can found Appendix E.
We develop three baseline algorithms for robot evolution by combing the three design optimization methods in Section 1.1 and PPO for control optimization in Section 4.2. Since the control optimization method is the same for all baseline algorithms, we simply use GA, BO, CPPN-NEAT to denote these three baseline algorithms with different design optimization methods. The evaluations of our baseline algorithms are performed on machines with Intel Xeon CPU @ 2.80GHz* 80 processors on Google Cloud Platform; GPU is not required. Evaluating one algorithm on single task usually takes several hours to twenty hours, depending on the number of evaluations, size of population, etc. See Appendix D for more details on hyperparameters of all the experiments.
5.1 Comparisons among baseline algorithms
We plot the reward curves of the three baseline algorithms on 10 selected benchmark tasks in Figure 3. There no single optimal algorithm that performs the best on all tasks, but overall, GA outperforms the other two baseline algorithms. This is surprising because our genetic algorithm is implemented with simple and intuitive operators for mutation and selection without sophisticated mechanisms. Therefore, we believe that with more carefully designed operators, GA has the potential to evolve much more intelligent robots. CPPN-NEAT generally performs well on locomotion tasks, as tested by previous works, but performs poorly more complex manipulation tasks. This is possibly because
7

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NEAT favors CPPNs with simpler structures, which encourages CPPNs to generate robots with more regular patterns. However, succeed in complex manipulation tasks, some agile substructures of the robot must evolve, which might only exist in robots with irregular patterns. Finally, it is not surprising that BO performs poorly on most of the tasks because high-dimensional categorical input parameter space and the noisy evaluation done by RL together pose challenge to fitting an accurate surrogate model in BO.
Walker-v0
BridgeWalker-6
Climber-v0
UpStepper-vO
Traverser-vo
10.5
6
Reward
9.5
100
400
400
Carrier-v0
Thrower-v0
BeamSlider-vd
Catcher-vO
Lifter-v0
3.0
1.0
Reward
2.5
-1.5
2.0
-3.0
600 Evaluations
200 Evaluations
Evaluations
Evaluations
Evalustions
GA
CEAN
BO
Figure 3: Performance comparison among baseline algorithms. We plot the best performance of robots that each algorithm has evolved w.r.t. the number of evaluations on each task. All the curves averaged over 6 different random seeds, and the variance is shown shaded region.
Carrier
Lifter
Bridge Walker
Gen 1
Gen 10
Gen 20 1:10.
Gen 1 R:0.0
Gen 15 R:0.1
Gen 30
Gen 1 R:3.8
Gen 5
Gen 15 R.6.6
1.3
Figure 4: Evolution of robot designs. For each of the three selected tasks, we visualize the population three different generations. Each column corresponds to one generation which we show four top performing robots along with their average reward.
5.2 Evolution analysis
In Figure we visualize the top four robots in three different generations training the genetic algorithm for the Carrier, Lifter, and Bridge Walker task. We also show the average reward these designs achieve.
In the carrier task, the robot must catch an object that falls from above and then carry that object as far as possible. Therefore, successful design for this task achieves two main goals 1) allowing the

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Carrier (Easy)
Climber (Medium)
Hand-Designed
Hand-Designed
Optimized Design
Hand- Hand-Designed Designed
Hand- Hand-Deskee Designed
Hand- Hand-Designed Designed
Optimized Design
Slide Beam (Hard)
Hand-Designed
Designd
Hand-Designed
Optimized Design
Figure 5: Comparison between algorithm-optinie robots and hand designed robots on three tasks. In each task, visualize one robot optimized by the algorithm and several hand-designed robots.
robot to catch and hold the object securely 2) allowing the robot to move fast. We observe that robots with a block-holding mechanism and with legs are selected for in the top survivors of generation I (randomly initialized). As evolution progresses, these structures become increasingly optimized. Specifically, in later generations, the robots' structures allow them walk faster while still preventing the block from falling.
similar comparison pattern can be seen in the Lifter task, where the algorithm learns a parallel gripper-like shape underneath the robot in order to manipulate an object. Unlike in the carrier task, the design structures that the algorithm generates are not prominently found the initial generation. Finally, these patterns are echoed in the Bridge Walker task. Here the robot learns to evolve large front foot maximize its surface area and friction force best walk across the soft rope bridge.
5.3 Comparison against hand-designed robots
We compare the performances of robots optimized by algorithm and the hand designed robots several tasks to show the necessity of a co-design algorithm (Figure 5). The structure of the hand designed robots bio-inspired and manually constructed according control are optimized by PPO.
best intuition, and their
For every task, the hand designed robots outperformed by at least one algorithm (usually more). For instance, for the Climber task we tested numerous natural robot designs. However, none of them successfully climbed very far. The issue with designs that could not find the right trade off between getting traction on the wall, and accelerating upwards. The genetic algorithm, however, is able to find this balance. develops leg-like structures that help the robot make forward progress, as well as long flat back that maximizes contact/frictional forces with the wall. Additionally. the genetic algorithm selects for having hole in the center of its body, which helps it achieve certain optimized walking motion.
For other tasks, the performance between the hand designed robots and the robots produced by the algorithms is quite comparable. This the case with the Carrier robots, as a very natural hand-designed Carrier robot performs almost well the best optimized robots produced by the design-optimizatioo algorithms.
In the final case, there are tasks where neither a hand designed nor robot produced by the algorithm could achieve satisfying performance. One such environment is the Beam Slider environment. For this task, many of the hand design robots fail to even achieve the first part of the goal and position themselves underneath the beam. While there one robot produced by the genetic algorithm that does slide the beam across several pegs, from visual observation we believe it comes nowhere close

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to exhibiting the optimal behavior in this environment. This suggests that further work is needed in designing co-optimization algorithms that can complete these hard tasks.
Conclusion and future work
In this paper we proposed Evolution Gym, the first large-scale benchmark for evolving the structure and control of soft robots. Through the wide spectrum of tasks in Evolution Gym, systematically studied the performance of current state-of-the-ar co-design algorithms. As a result, we observed how intelligent robots could be evolved autonomously from scratch yet still be capable of accomplishing some surprisingly complex tasks. We also discovered the limitations of existing techniques for evolving more intelligent embodied systems.
There are several potential directions to explored in the future. First, with the help of our proposed benchmark, it is desirable to develop more advanced co-design algorithms to solve the difficult tasks which existing methods cannot address. Our currently implemented baseline algorithms share a bi-level optimization routine where the design optimization is in the outer loop while the control optimization is in the inner loop. However, Evolution Gym is agnostic to the specific training procedure used. As result, some ideas for future work using our framework could include coneur- rently co- co-optimizing the design and control, neuroevolution algorithms, morphogenetic development, gradient-basee methods for design optimization, algorithms with decentralized controllers.
Second, a robot will considered more successful if can perform multiple tasks. Our benchmark suite naturally provides comprehensive set of tasks and can potentially promote more exciting research work about multi-task or multi-objective robot co-design algorithms.
Another consideration is the specific morphological encodings used by the codesign algorithms as more intelligent encodings could lead to better performance. For instance. [38] analyzes the strengths and weaknesses of different morphological encodings. Our baseline algorithms use a direct encoding and CPPN but exploring other encoding representations remains interesting future work.
Finally, since tasks in Evolution Gym are currently limited to either locomotion or manipulation, we plan to further extend Evolution Gym to additional task categories such flying swimming by incorporating new simulation capabilities.
Overall, we believe our carefully benchmarking tool fills an important missing piece in research in soft robotics and robotic evolution algorithms. Armed with the flcxible and expressive framework Evolution Gym provides, we are optimistic that future researchers will use Evolution Gym as a standard test bed to improve co-design methods and evolve more intelligent robots.
Societal Impact
We regard this work as very preliminary piece of research in the field of soft robot co-design, and therefore think that are still far away from causing harm to society. However, we can definitely foresee some problems if this technology were to be applied in the real world on a large scale. For instance, this work may inspire the automatic design of real biological creatures in which serious ethical issues exist. Additionally, since the users have full control over the reward design when customizing the benchmark environments, they could specify pernicious goals and encourage the co-design algorithm to produce more biased results.
Acknowledgments and Disclosure of Funding
We thank Tao Du and the anonymous reviewers for their helpful comments in revising the paper. This work is supported by the Defense Advanced Research Projects Agency SPA8770-200C-0077
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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Section
(c) Did you discuss any potential negative societal impacts of your work? [Yes] See Section 6.
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [N/A]
(b) Did you include complete proofs of all theoretical results? [N/A]
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or URL)? [Yes] The URL is presented in the abstract.
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix D.
(c) Did you report error bars (e.g., with respect to the random seed after running exper- iments multiple times)? [Yes] We ran experiments with multiple random seeds and reported error bars. See Section
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, cloud provider)? [Yes] See Section 5.
4. If you using existing assets (e.g., code, data, models) or curating/releasing
assets... (a) If your work uses existing assets, did you cite the creators? [Yes]] The existing code implementation for our bascline algorithms are cited in Section 4.
(b) Did you mention the license of the assets? [Yes] This benchmark platform will be released under the MIT license. See Section 1.
(c) Did you include any assets either in the supplemental material URL? [Yes] The URL presented in the abstract.
(d) Did you discuss whether and how consent was obtained from people whose data you'le 're using/curating? [N/A]
(e) Did you discuss whether the data you are using/curating contains personally identifiable information offensive content? [N/A]
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, applicable? [N/A]
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A]
(c) Did you include the estimated hourly wage paid participants and the total amount spent on participant compensation? [N/A]
14


Reviews:
[{"review_id": "fk1x2nNlsa", "reviewer": "Reviewer_1ku4", "summary": "In the paper, \"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots\", the authors introduce a new framework for the benchmark for the evolution of the design of soft robots. This framework, called \"Evolution Gym, contains 30 environments (only 10 are described in the main paper), which cover different types of tasks (locomotion, manipulation, and a mixture of both) and different level of difficulties. In addition to the introduction of the framework, the paper also presents experimental results in which the authors have implemented three baseline algorithms (Genetic Algorithm, Bayesian Optimisation, and CPPN-NEAT) for the optimisation of the robot's morphology and PPO for the optimisation of the controllers.", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper is well written, clearly structured and easy to follow. The illustrations are also very nice and enable the readers to easily understand the objective of the different tasks. I believe that the central idea of the paper of introducing a novel common benchmark for the evolution of soft robots is a great idea. I hope this will have the same impact as the OpenAI Gym environment had on the reinforcement learning community. Overall, I enjoyed reading the paper.\n\nHowever, I have two main concerns with the paper. I structured these comments in points and subpoints to make future discussions easier:\n1) I expected the paper to be focused on the proposed framework/benchmark implementation: \n1.1) In particular, there are some claims like \"user-friendly Python interfaces\", \"expressive multi-material voxel-based structure design space\", \"flexibility of the controller parametrisation\" that are not much supported by the paper. What makes the interface more or less user friendly than other tools? What are the limits, or advantages on the expressivity of the morphological encoding used in this benchmark compared to the literature? Is it using the state-of-the-art or even innovating on top of it? Etc.\n1.2) An important aspect of such tools is the documentation, but nothing is said about this in the paper. \n1.3) After reading the paper, there are multiple questions that remain unanswered to me: \nWhat are the advantages of using this framework compared to other existing ones? Is it offering any performance improvement over alternatives? \n\n2) The experimental comparison is not adding much to the paper when we consider this is taking more than a third of the paper. The considered baselines are not novel, but instead, well-established algorithms (as clearly stated by the authors). This is an excellent point to provide existing baselines with such benchmark tools (similar to Stable baselines in Deep RL). \n2.1) However, I believe it would have been interesting to link these algorithms with existing papers attempting to apply these baselines in similar domains and demonstrating that the implemented baselines can act as reproductions of these previous results. This would provide tools to researchers to try to build on top of these algorithms. \n2.2) The main outcome of the experimental section is in my opinion the relative difficulty order of the different tasks. It is particularly interesting to see that some tasks remain unsolved, which represents a nice challenge for future work. However, it seems that we are observing the expected results based on the literature (i.e., BO does not scale well, GA is slow but can lead to very good results and CPPN-NEAT is very domain-dependent). \n\nOverall, the paper proposes an excellent tool for researchers. Yet, I believe the paper can be made significantly stronger by focusing more on the framework/benchmark and its advantages (which needs to be demonstrated) than on the experimental evaluation of well-established algorithms. ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors discuss the limitations of their approach and I cannot see any direct societal impact of their work.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "QGn6b7TwtA", "reviewer": "Reviewer_t5kh", "summary": "The paper presents a benchmark for soft robotics along with several baseline implementations. The benchmark creation is carefully designed and it is clear that it will be a useful resource for the Evolution robotics, RL, and co-adaptation community.", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This paper solves an important problem -- a benchmark + standard baseline implementation for soft robot evolution. The benchmark is carefully constructed and follows a line of past research. The ultimate test is how well and often do people use it as a benchmark. I do not have any serious objections to this paper. A few minor questions and feedback are as follows.\n\n1. Lines 30-31. I am not sure if this is entirely accurate. Some recent work such as Hejna et al. 2021 shows co-adaptation for arbitrary tasks.\n2. Time analysis. How slow/fast is the simulator compared to mujoco or other standard benchmark?\n3. 2d vs 3d. Please comment on extending such environments for 3D environments.\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "N/A Early stage research.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2 hours. "}, "extra_text": {}}, {"review_id": "Mjkcnpm0FuV", "reviewer": "Reviewer_rqAv", "summary": "This paper proposes Evolution Gym, a simulation environment and benchmark which may be used for evaluating design and control optimization techniques for soft robots built in a voxelized design space. In addition to the benchmark, the paper compares several existing design and control optimization techniques for a common set of tasks.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper posits that co-design algorithms are understudied due to the complex, bi-level nature of the underlying problem and the lack of a benchmarking platform. It seems that a benchmarking platform which could be used for co-design of robots would be a useful tool to roboticists. In my opinion, such a platform could be used to gain understanding of some key concepts underlying soft robots (e.g. the role of morphological intelligence) while providing clear baselines of optimization techniques and behavior which will establish a state-of-the-art. This contribution has the potential to be important to soft robotics.\n\nHowever, some aspects of the provided benchmark and comparison are underdeveloped. In general, a stronger case could have been made for the particular choice of physics, design space, and the presence of feedback in the proposed benchmark. Further, some clarifications are needed about the optimization comparison.\n\n1. The approach given has a key sim-to-real limitation that is not discussed. Details are given in the following section.\n\n2. Why are voxel-based design spaces the best choice? It is claimed that voxel-based soft robots offer a \"general and universal representation\" of various \"categories\" of robot designs, yet it is not clear which categories the learned robots fit into. Indeed, it is not even clear at present how to physically construct voxel-based soft robots under this general representation (e.g. Kriegman et al, 2020, Scalable sim-to-real transfer of soft robot designs). Authors might consider commenting on learned systems, noting whether actuators presently exist that allow these robots to be constructed. Is it better to have a generalized representation such as this one, or many possible constrained design spaces that are more directly inspired from existing soft robot designs? Could robots learned in Evolution Gym guide soft actuator development? Detailed discussion of this point could help readers.\n\n3. Why does a benchmark need to include feedback control? The paper notes that prior co-design work in soft robotics mainly has mainly explored open-loop control, while the proposed framework allows detailed high-dimensional observations to be used as feedback. This engages directly with ideas of morphological computation (e.g. Tolley and Rus, 2015) and its role in soft robotics; the simulator could allow experiments about which balance between morphological computation and feedback is best. Yet, here, it is not clear how much feedback the learned controllers are using and whether the inclusion of it was needed. Comments about this issue, and how the proposed benchmark may help with its study, would better show the impact of the work.\n\n4. It seems somewhat surprising that the plain GA outperforms the other methods in several tasks, given that GAs have not been shown to converge to global minima. Comments on this success might help readers.\n\n5. Is it possible to use gradient-based methods for design optimization with this benchmark? Would anyone need to do so in the given design space? Please clarify.\n\n6. Why is a simplified, 2D spring-based physics representation best? It is clear that such an approach is faster computationally than other mechanics methods (e.g. Finite Element Analysis or particle-based methods), but accuracy also suffers if the approach is simplified. Why not take a more computationally heavy (but accurate) approach and wait for Moore's Law? Please discuss. Further, does this physical simulation differ from the one used by Medvet et al 2020 (citation 21)? Please clarify.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "One key limitation of a this simplified benchmark environment is that its gap with reality is not yet characterized. The paper states that \"we observed how intelligent robots could be evolved autonomously from scratch yet still be capable of accomplishing some surprisingly complex tasks.\" While this did happen in the simulation environment, it is not clear that real devices built with the best-performing designs and controllers would truly perform best in the real world. As noted above, it is unclear whether the result robots are easy to construct, and it is further unclear whether sensing technologies are possible for the type of observations described in Section 3.3. While a detailed experimental validation of all proposed robots is clearly out of scope, some comments on the reality gap and the transition from 2D to 3D are \nnecessary given the embodied nature of the problem.", "review_text": "", "extra_scores": {"ethical_concerns": "No ethical concerns.", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes a simulation-based benchmark for learning and evaluating algorithms that jointly evolve the design and control of soft robot bodies. The benchmark includes a suite of environments involving locomotion and manipulation tasks. The benchmark includes implementations of several existing design-control optimization methods and the paper presents experimental results for these baselines on a subset of the tasks.

Jointly optimizing a robot's physical design together with its control policy is a challenging problem that has recently gotten the attention of many researchers in the machine learning community. These challenges are exacerbated in the case of soft-bodied robots due to the significantly richer design space and the complexity of the physics. Owing to this and the renewed attention paid to joint optimization of design+control, the availability of a benchmark suite for soft robots would provide a valuable contribution. The reviewers agree that the paper is well motivated and well written. However, there are concerns about some of the assumptions and claims made in the paper, notably the sufficiency of a voxel-based representation of the design space, the simplistic physics, and a lack of a discussion of the sim-to-real gap, which seems to be exacerbated by the simplicity of the physics model and the nature of the design space. Thus, it is unclear which communities this benchmark is targeting. One can certainly see it being used as a simulation benchmark suite for the (robot) learning community, but the extent to which it will benefit soft roboticists appears limited.
Decision: Accept (Poster)