Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation (2408.16069v1)

Abstract: Animals can accomplish many incredible behavioral feats across a wide range of operational environments and scales that current robots struggle to match. One explanation for this performance gap is the extraordinary properties of the biological materials that comprise animals, such as muscle tissue. Using living muscle tissue as an actuator can endow robotic systems with highly desirable properties such as self-healing, compliance, and biocompatibility. Unlike traditional soft robotic actuators, living muscle biohybrid actuators exhibit unique adaptability, growing stronger with use. The dependency of a muscle's force output on its use history endows muscular organisms the ability to dynamically adapt to their environment, getting better at tasks over time. While muscle adaptability is a benefit to muscular organisms, it currently presents a challenge for biohybrid researchers: how does one design and control a robot whose actuators' force output changes over time? Here, we incorporate muscle adaptability into a many-muscle biohybrid robot design and modeling tool, leveraging reinforcement learning as both a co-design partner and system controller. As a controller, our learning agents coordinated the independent contraction of 42 muscles distributed on a lattice worm structure to successfully steer it towards eight distinct targets while incorporating muscle adaptability. As a co-design tool, our agents enable users to identify which muscles are important to accomplishing a given task. Our results show that adaptive agents outperform non-adaptive agents in terms of maximum rewards and training time. Together, these contributions can both enable the elucidation of muscle actuator adaptation and inform the design and modeling of adaptive, performant, many-muscle robots.

• The paper introduces a novel simulation model that captures exercise-induced muscle strengthening to improve control in biohybrid systems.
• The reinforcement learning controller, using a PPO algorithm, coordinates 42 muscle actuators to navigate toward eight distinct targets.
• The integrated design approach accelerates training efficiency and streamlines the identification of key actuators for optimized robotic fabrication.

Reinforcement Learning and the Adaptive Control of Biohybrid Robots

The paper entitled "Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation" presents a sophisticated exploration of biohybrid robotics through the integration of natural muscle adaptability and reinforcement learning. Authored by researchers at Carnegie Mellon University, this paper explores the fundamentals of using biological muscle tissues within robotic systems to achieve enhanced adaptability and operational performance, traits inherently present in biological entities but elusive in traditional robotic systems.

Overview of Biohybrid Robotics Challenges

Biohybrid robotics seeks to mimic the extraordinary capabilities of animal systems by integrating biological materials, such as muscle tissues, into robotic frameworks. This approach promises benefits such as self-healing capacities, compliance, and biocompatibility. However, a substantial challenge remains: the force output of these muscle actuators is transient and changes over time, as they become stronger through exercise—a property that complicates their modeling and control in robotic applications.

Research Contributions

The focal point of this paper is the incorporation of muscle adaptability into biohybrid robot design using reinforcement learning, both as a co-design tool and a system controller. The research features:

1. Modeling Muscle Adaptation: The paper introduces a novel approach to model biohybrid muscle actuation that accounts for use-history-dependent strength changes. By embedding adaptability into the simulation framework, the paper advances biohybrid modeling closer to biological realities.
2. Reinforcement Learning as a Control Mechanism: An off-the-shelf Proximal Policy Optimization (PPO) algorithm is employed to manage the complex task of coordinating 42 muscle actuators on a worm-like lattice structure. This RL agent successfully navigates the robot toward eight distinct targets, demonstrating the flexibility and effectiveness of RL in biohybrid contexts.
3. Co-Design Partner: The reinforcement learning framework not only controls the biohybrid system but also serves as a diagnostic tool to identify critical actuators for specific tasks, streamlining the resource-intensive fabrication process.

Numerical Results and Their Implications

The paper highlights how adaptive agents outperform non-adaptive counterparts regarding training efficiency and maximum achieved rewards. This indicates that adaptability introduces a beneficial dynamic akin to curriculum learning, where the agent masters simpler environments before confronting more complex scenarios. The improvement is especially pronounced for targets that demand greater reach, underscoring the utility of adaptability in complex robotic tasks.

Future Directions and Implications

This research marks a significant stride in bridging biohybrid and adaptive control systems, pointing toward practical applications in making more efficient, robust, and sophisticated biohybrid robots. However, it also opens avenues for further work in several areas:

• Refinement of Muscle Adaptation Models: While the current model reflects muscle strengthening contingent on use, capturing fatigue and atrophy dynamics would enrich the fidelity and usefulness of simulations.
• Physical Realizations: Experimental validation of these simulated insights could accelerate the translation of biohybrid robots from labs to real-world scenarios, thereby addressing challenges in sectors like biomedicine and environmental monitoring.
• Advanced Control Strategies: Further research into the refinement of reinforcement learning algorithms tailored for biohybrid systems could uncover even more effective strategies for managing large numbers of independent actuators.

Conclusion

This paper represents an insightful investigation into the symbiotic potential of bioengineering and artificial intelligence. By adeptly weaving together aspects of adaptability inherent in biological systems with state-of-the-art RL methodologies, the research not only enhances our understanding of biohybrid robotics but also paves the way for future innovations that can fully harness the adaptive characteristics of biological materials. Such developments promise to transform not only robotics but also the understanding of embodied intelligence within engineered systems.