Designing Robots That Evolve

Author: Denis Avetisyan

A new survey explores how jointly optimizing a robot’s body and brain is unlocking truly adaptable artificial intelligence.

The proliferation of co-designed embodied agents-manifesting across both simulated and physical realms, and drawing from diverse precedents in fields ranging from evolutionary robotics to differentiable aquatic locomotion-demonstrates an emerging ecosystem where design isn’t about imposition, but about seeding environments and anticipating the inevitable patterns of adaptation and failure inherent in complex, embodied systems.

This review presents a taxonomy, current frontiers, and open challenges in the field of embodied co-design for rapidly evolving agents.

While traditionally robotics and artificial intelligence have treated body and control as separate design challenges, recent advances suggest synergistic optimization is key to truly adaptable intelligence. This survey, ‘Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges’, provides a comprehensive overview of embodied co-design (ECD), a paradigm focused on jointly evolving agent morphology and control systems. Through a novel hierarchical taxonomy encompassing bi-level, single-level, generative, and open-ended frameworks, we synthesize over a hundred studies to reveal current methodologies and benchmarks in both simulation and real-world applications. Given the increasing demand for robust and versatile robots, how can we best leverage ECD to unlock the full potential of embodied intelligence and facilitate genuinely open-ended learning?

The Inevitable Disconnect: Functional Segregation and its Costs

For decades, robotic engineering has largely operated under a principle of functional segregation – the physical construction, or morphology, of a robot is designed independently from the algorithms governing its behavior, its ‘brain’. This historical approach, while simplifying the engineering process, often results in robots with suboptimal performance and limited adaptability. The disconnect between a robot’s physical form and its control system forces complex computations to overcome physical limitations – a robot might require intricate programming to navigate a simple obstacle because its body wasn’t designed to facilitate such movement. Consequently, robots struggle with tasks demanding nuanced physical interaction or operation in unpredictable environments, hindering their ability to effectively respond to real-world challenges and limiting the potential for truly intelligent, embodied agents.

A fundamental challenge in robotics stems from the historical division between an agent’s physical form and its control systems; this disconnect effectively creates a barrier to proficient task completion. When morphology and behavioral intelligence are designed in isolation, the resulting robot often struggles with complexities inherent in real-world scenarios. The body’s capabilities – its strength, reach, or dexterity – aren’t fully utilized by the control algorithms, and conversely, sophisticated software can be hampered by limitations in the physical platform. Consequently, robots may require excessive computational power or exhibit awkward, inefficient movements when attempting tasks that a human – or even a simpler, organically integrated organism – could accomplish with ease. This separation ultimately restricts the development of truly adaptable and robust robotic systems capable of navigating and interacting with dynamic environments.

As tasks grow increasingly complex – demanding nuanced interaction with unpredictable environments – reliance on purely computational solutions proves increasingly inefficient. Modern robotics is therefore shifting towards designs where an agent’s physical form, or embodiment, is not merely a vessel for computation, but an integral component in simplifying problem-solving. By strategically leveraging morphology – the shape, material, and mechanics of the robot – researchers are discovering ways to offload computational burden onto the physical world. For example, a robot designed with compliant joints can passively adapt to external forces, reducing the need for complex feedback control loops. This principle, known as morphological computation, allows agents to perform tasks requiring high precision or adaptability with significantly reduced processing power, ultimately enhancing both efficiency and robustness in real-world applications.

Four distinct embodied co-design frameworks-DERL, Softzoo, EvolutionGym, and RoboGrammar-demonstrate diverse approaches to creating agents through interactive design processes.

The Unified Imperative: Co-Design as Systemic Optimization

Embodied co-design represents a unified optimization approach where an agent’s physical structure, or morphology, and its control policy are simultaneously optimized to achieve improved performance. This contrasts with traditional robotics where morphology is often fixed and control is designed subsequently. By treating morphology as a tunable parameter within the optimization process, synergistic gains are realized as physical characteristics can be adapted to simplify control tasks and enhance overall efficiency. Demonstrated applications of embodied co-design span diverse areas including locomotion, manipulation, and soft robotics, consistently exhibiting improvements in metrics such as speed, robustness, energy efficiency, and adaptability to novel environments compared to independently designed systems.

Navigating the combined design space of morphology and control requires algorithms capable of handling high dimensionality and non-differentiability. Traditional optimization methods often struggle with the discrete nature of morphological designs and the continuous parameters of control policies. Evolutionary algorithms, such as genetic algorithms and covariance matrix adaptation evolution strategy (CMA-ES), are frequently employed due to their ability to explore complex, black-box search spaces without requiring gradient information. Reinforcement learning techniques, particularly those incorporating novelty search or intrinsically motivated exploration, can also be utilized, although they often require substantial computational resources. The challenge lies in efficiently searching for Pareto-optimal solutions that balance morphological cost, control complexity, and performance objectives, often necessitating the use of surrogate models or dimensionality reduction techniques to manage computational demands.

Integrating morphology directly into the control system design allows for the distribution of computational demands from the controller to the physical structure. This is achieved by leveraging the body’s mechanics to perform tasks that would otherwise require complex computations within the control policy. Specifically, appropriately designed physical attributes can passively stabilize the agent, reduce the need for active control, and simplify the required control algorithms. This morphological computation reduces the controller’s workload, leading to lower computational costs and decreased latency, while also enhancing robustness to disturbances and model inaccuracies as the physical structure inherently provides a degree of error tolerance.

The embodied co-design landscape encompasses foundational elements and various frameworks for collaborative design processes.

The Algorithmic Toolbox: From Bi-Level to Generative Futures

Bi-level optimization is an iterative process used in co-design problems, initially fixing the morphological design and optimizing the control policy for that specific structure. This first stage typically employs techniques such as gradient descent or reinforcement learning to find an optimal control strategy, given the constraints of the fixed morphology. Following this, the morphology itself is modified based on the performance achieved with the optimized control policy. This refinement stage adjusts parameters defining the structure-such as link lengths, joint angles, or material properties-and then the process repeats, re-optimizing control for the new morphology. This cycle continues until a satisfactory balance between morphological design and control performance is achieved, effectively searching for co-optimized solutions through alternating optimization loops.

Single-level optimization techniques streamline the co-design of morphology and control by formulating the problem as a unified optimization task. This contrasts with bi-level approaches which separate the optimization into iterative loops. In single-level optimization, both morphological parameters and control policies are treated as variables within a single objective function, typically expressed as $J(m, c)$, where $m$ represents morphology and $c$ represents control. Gradient-based methods, or derivative-free optimization algorithms, are then applied to directly minimize or maximize this function, simultaneously adjusting both morphological features and control strategies. This direct approach can lead to faster convergence and the discovery of solutions that might be missed by iterative bi-level methods, although it often requires a more complex parameterization and larger search space.

Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) are becoming prevalent in robotic and biomechanical design due to their ability to efficiently generate a range of plausible morphological variations. Traditional optimization methods often require evaluating numerous discrete designs, which can be computationally expensive. Generative models, however, learn a latent space representation of the design space, enabling the generation of new designs by sampling from this space. This approach drastically reduces the search space and accelerates the co-design of morphology and control by allowing for the rapid exploration of diverse, yet feasible, body plans. Furthermore, the latent space can be directly optimized using gradient-based methods, streamlining the process of finding optimal designs for specific tasks and performance criteria.

Reinforcement learning (RL) algorithms are integral to the development of effective control strategies for robots and systems with optimized morphologies. These algorithms enable agents to learn optimal policies through trial and error, maximizing cumulative rewards within the defined morphological space. Common RL approaches employed include Q-learning, policy gradients (e.g., REINFORCE, PPO, DDPG), and actor-critic methods. The state space for the RL agent typically incorporates sensory inputs representing the system’s configuration and environment, while the action space defines the controllable degrees of freedom. Successful implementation requires careful design of the reward function to incentivize desired behaviors and avoid unintended consequences. Furthermore, techniques like experience replay and target networks are often used to stabilize learning and improve sample efficiency, particularly in high-dimensional control tasks.

Embodied co-design extends traditional control design by integrating physical embodiment to achieve more robust and adaptable solutions.

Bridging the Divide: From Simulation to Real-World Resilience

The successful deployment of artificial intelligence and robotic systems in the physical world is frequently hampered by the challenges of sim-to-real transfer. While agents can achieve high performance within the controlled confines of a simulation, these capabilities often fail to translate effectively to real-world scenarios. This performance degradation stems from inherent discrepancies between the simulated and real environments – differences in physics, sensor noise, lighting conditions, and unforeseen environmental factors. Even subtle variations can significantly impact an agent’s ability to perceive, interpret, and interact with its surroundings, leading to errors and instability. Consequently, bridging this “reality gap” remains a critical area of research, demanding innovative approaches to enhance the robustness and adaptability of AI systems before they can reliably operate outside of the virtual realm.

Domain randomization addresses the challenge of transferring learned behaviors from simulation to the complexities of the real world by intentionally varying simulation parameters during training. This technique doesn’t attempt to create a perfectly accurate simulation; instead, it exposes the learning agent to a wide distribution of plausible environments – altering factors like lighting, friction, object textures, and even physics properties. By training across this randomized spectrum, the agent is compelled to develop robust control policies that aren’t overly reliant on specific simulated conditions. Consequently, when deployed in a real-world setting, the agent exhibits improved generalization capabilities and a reduced susceptibility to the inevitable discrepancies between the simulated and actual environments, effectively bridging the reality gap and fostering more reliable performance.

Differentiable simulation represents a paradigm shift in robotics and agent design, allowing for the optimization of both an agent’s physical morphology and its control algorithms through gradient descent. Traditionally, these aspects were designed and tuned separately, often relying on laborious manual adjustments or computationally expensive trial-and-error methods. However, by constructing a simulated environment where every step is mathematically differentiable, researchers can calculate how changes to an agent’s shape or control parameters impact its performance, and then automatically adjust those parameters to maximize that performance. This allows for the creation of agents that aren’t merely programmed to perform a task, but are designed to excel at it, leading to remarkably adaptable and resilient systems capable of navigating complex and unpredictable environments. The technique effectively turns the design process into an optimization problem, enabling the automated discovery of novel and effective solutions that might otherwise remain undiscovered.

The convergence of simulation-based design with human expertise promises a significant acceleration in the development of robust and adaptable robotic systems. While techniques like domain randomization and differentiable simulation offer powerful automated design capabilities, they benefit immensely from the nuanced judgment and intuitive problem-solving skills of human engineers. Collaborative workflows, where algorithms generate and refine designs, and humans provide critical feedback and guide the exploration of promising solutions, allow for a more efficient search of the design space. This synergy not only speeds up the iteration process but also enables the incorporation of tacit knowledge and real-world constraints that are difficult to encode directly into algorithms, ultimately leading to more practical and effective robotic designs. The resulting systems are poised to exhibit enhanced resilience and performance in complex, unpredictable environments.

The Trajectory of Intelligence: Adaptability and the Promise of Lifelong Learning

Agents equipped with lifelong learning algorithms transcend the limitations of static programming by continuously refining their capabilities throughout operation. These systems don’t merely execute pre-defined tasks; instead, they accumulate knowledge from interactions with dynamic environments and evolving objectives. This continuous adaptation manifests as improved performance over time, allowing the agent to not only maintain functionality in the face of change, but to actively optimize its strategies. Unlike traditional machine learning models requiring retraining from scratch with new data, lifelong learning enables incremental knowledge acquisition, fostering resilience and minimizing disruption. The result is an agent capable of navigating unforeseen circumstances, generalizing to novel situations, and persistently improving its proficiency – a crucial step towards truly intelligent and autonomous systems that can operate effectively in the real world.

The convergence of lifelong learning and embodied co-design promises a new generation of adaptable agents capable of continuous self-improvement. Rather than being pre-programmed for specific tasks, these systems dynamically refine both their physical form – their morphology – and the strategies they use to control it, known as their control policy. This co-evolutionary process allows an agent to respond effectively to unforeseen circumstances and changing demands. Through continuous interaction with the environment, the agent can optimize its body plan for efficiency, stability, or specialized function, while simultaneously honing its control mechanisms to leverage those physical adaptations. This synergistic relationship between body and brain fosters exceptional resilience, enabling agents to not merely survive, but thrive, in unpredictable and dynamic landscapes, opening doors to truly autonomous and versatile robotics.

Recent advances in soft robotics are revolutionizing the field of embodied intelligence by moving beyond rigid, traditional designs. Utilizing highly deformable materials – such as elastomers, gels, and fluids – these robots can dynamically alter their shape, allowing them to navigate complex terrains and interact with delicate objects in ways previously impossible. This inherent flexibility not only enhances adaptability but also simplifies control strategies; rather than requiring precise commands for each joint, soft robots often leverage the material properties themselves to achieve desired movements. The resulting designs are often more robust, energy-efficient, and capable of performing intricate tasks with fewer actuators, opening doors for applications in areas like minimally invasive surgery, search and rescue, and bio-inspired locomotion. This shift towards compliant machines promises a future where robots seamlessly integrate into unstructured environments and collaborate with humans in increasingly natural and intuitive ways.

The potential for coordinated action dramatically increases when multiple embodied agents utilize co-design principles. Rather than relying on pre-programmed interactions, these systems allow agents to dynamically adjust their morphology and control strategies in response to each other and the environment, fostering emergent collective behaviors. This distributed adaptation proves particularly valuable in tackling complex challenges – consider a swarm of robots collaboratively mapping a disaster zone, or a team of soft robotic arms assembling intricate structures. Through continuous interaction and mutual refinement, these multi-agent systems demonstrate resilience to individual failures and an ability to discover solutions that would be difficult, if not impossible, to engineer directly, paving the way for robust and adaptable robotic teams capable of tackling real-world problems with increased efficiency and innovation.

The pursuit of adaptable agents, as detailed in this survey of embodied co-design, echoes a fundamental truth about complex systems. It isn’t about achieving a final, perfected form, but fostering an environment where continuous adaptation is possible. This resonates with Ken Thompson’s observation: “There’s no perfect architecture, just a willingness to change.” The article champions a move beyond static designs, embracing the interplay between morphology and control, acknowledging that true robustness stems not from rigid planning, but from the capacity to evolve. Scalability, often touted as a goal, seems merely a justification for accepting ever-increasing complexity, a complexity best managed not by control, but by generative processes that allow the system to discover its own solutions.

What’s Next?

The taxonomy of embodied co-design, as presented, feels less like a map and more like a provisional census of a migrating species. Each optimized morphology, each learned gait, is a fleeting adaptation, a local optimum soon to be challenged by a shifting landscape of constraints. The pursuit of ‘open-ended evolution’ risks becoming an exercise in generating novelty for its own sake, mistaking interesting failures for genuine progress. The system doesn’t reveal its limits; it becomes the limit.

The core challenge isn’t merely automating design, but accepting the inevitability of redesign. The coupling of morphology and control isn’t a solution; it’s a deferral. The true frontiers lie not in more efficient algorithms, but in architectures that gracefully accommodate obsolescence. Logging isn’t about tracking performance; it’s about documenting the pre-history of failure, the whispers of future incompatibilities.

One suspects the most fruitful path involves relinquishing control – not in the sense of abandoning optimization, but in embracing the inherent unpredictability of complex systems. If the system is silent, it’s not resting. It’s accumulating the conditions for its own transformation. The goal isn’t to build an intelligent robot; it’s to cultivate a lineage of them, knowing that each generation will be, by necessity, a refinement of its predecessors’ shortcomings.

Original article: https://arxiv.org/pdf/2512.04770.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/