Beyond Rigidity: A New Era for Soft Robot Control

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Author: Denis Avetisyan

Researchers are developing control frameworks that embrace the inherent flexibility of soft robots, moving beyond traditional methods designed for rigid machines.

A unified control framework accommodates the diverse morphology of soft robotic systems-from articulated arms to bioinspired aquatic and insectile designs-offering a means to navigate varied environments while prioritizing safety through a morphology-independent approach to control.
A unified control framework accommodates the diverse morphology of soft robotic systems-from articulated arms to bioinspired aquatic and insectile designs-offering a means to navigate varied environments while prioritizing safety through a morphology-independent approach to control.

This review details a novel approach leveraging ‘control compliance’ and sampling-based techniques for robust, safe, and generalizable control across diverse soft robotic systems and environments.

Despite rapid advances in soft robotics, a unified control framework remains elusive, with performance often tethered to specific morphologies and actuation schemes. This limitation motivates our work, ‘Toward generic control for soft robotic systems’, which proposes a novel approach grounded in ‘control compliance’-embracing, rather than suppressing, imprecise action representations. We demonstrate that leveraging this principle yields stable, safe, and transferable behavior across diverse robotic platforms, suggesting a path toward broadly applicable soft robot control. Could this paradigm shift unlock the full potential of soft robotics for real-world applications requiring adaptability and robustness?

The Inevitable Yield: Control Challenges in Soft Robotics

Conventional control systems, meticulously designed for rigid robots with predictable movements, encounter significant hurdles when applied to their soft robotic counterparts. The difficulty arises from a fundamental difference in structure: soft robots possess an almost infinite number of degrees of freedom, meaning countless ways to bend, twist, and deform. This contrasts sharply with the limited, well-defined movements of traditional robots. Furthermore, the materials used in soft robotics – elastomers, gels, and fabrics – exhibit highly nonlinear behavior; a small change in input doesn’t necessarily result in a proportional change in output, and the robot’s response can vary dramatically depending on its configuration. Consequently, mathematical models used to precisely predict and control rigid robots become inaccurate and ineffective, necessitating the development of entirely new control strategies capable of accommodating this inherent complexity and uncertainty.

Conventional robotics relies heavily on precise mathematical models built upon the assumption of rigid bodies and predictable movements; however, soft robots, constructed from highly flexible materials, fundamentally defy these principles. Their infinite degrees of freedom and nonlinear responses to stimuli render traditional control methods – designed for predictable, fixed structures – largely ineffective. Instead of striving for exact pre-programmed motions, successful control necessitates strategies that embrace adaptability. Researchers are exploring techniques like reinforcement learning and optimization algorithms that allow these robots to learn through interaction with their environment, effectively bypassing the need for detailed, accurate modeling. This shift prioritizes robustness and the ability to recover from unexpected disturbances, enabling soft robots to navigate complex and uncertain scenarios where precise prediction is impossible.

The effective manipulation of soft robots necessitates a move away from traditional control methodologies, which rely on precise mathematical models and assumptions of rigid body mechanics. These approaches falter when confronted with the infinite degrees of freedom and nonlinear behaviors characteristic of these compliant machines. Instead, researchers are increasingly focusing on robust, learning-based strategies, such as reinforcement learning and imitation learning, that allow robots to adapt and improve their performance through experience. This paradigm shift enables soft robots to navigate unpredictable environments and perform complex tasks without requiring detailed prior knowledge of their own dynamics or the external world. By embracing adaptability and learning, control systems can overcome the inherent complexities of soft robotics, paving the way for more versatile and resilient machines.

The successful navigation of control challenges in soft robotics promises a revolution across diverse fields. In medicine, these adaptable machines could enable minimally invasive surgical tools capable of maneuvering through delicate tissues and delivering targeted therapies with unprecedented precision. Beyond healthcare, soft robots offer unique advantages in hazardous environments; their pliable bodies allow them to squeeze through collapsed structures during search and rescue operations, assess damage in disaster zones, or explore challenging terrains inaccessible to conventional robots. Furthermore, the inherent safety of soft materials makes them ideal for human-robot collaboration, opening doors to assistive devices and co-bots that can work alongside people in manufacturing, agriculture, and daily life. This potential extends to environmental monitoring, underwater exploration, and even the development of bio-inspired prosthetics, highlighting the broad impact of overcoming the control limitations currently hindering the widespread adoption of soft robotic systems.

Inspired by human motor intelligence, the proposed control framework utilizes a learning-based dynamics model, a sampling-based planner, and an adaptive safety filter to achieve flexible yet stable soft robot control by mirroring the processes of coarse modeling, rapid planning, and reflexive safety regulation.
Inspired by human motor intelligence, the proposed control framework utilizes a learning-based dynamics model, a sampling-based planner, and an adaptive safety filter to achieve flexible yet stable soft robot control by mirroring the processes of coarse modeling, rapid planning, and reflexive safety regulation.

A Framework for Resilience: Integrating Learning, Safety, and Planning

The Soft Robot Control Framework is designed to provide stable and predictable control of soft robotic systems by integrating three core components: a learning-based model, robust safety assurances, and efficient trajectory planning. This framework utilizes a learned model, specifically a Neural Ordinary Differential Equation (Neural ODE), to represent the complex and often nonlinear dynamics inherent in soft robots. Crucially, the framework incorporates Control Barrier Functions within an Adaptive Safety Filter to enforce safe operational limits and prevent instability during execution. Finally, Sampling-Based Model Predictive Control (MPC) is employed to explore potential trajectories, optimize control inputs based on the learned dynamics and safety constraints, and ultimately achieve desired robot behaviors across a range of physical designs and environmental conditions.

The Soft Robot Control Framework employs a Neural Ordinary Differential Equation (Neural ODE) to model the robot’s dynamics, representing the time evolution of the robot’s state as a continuous transformation. This approach differs from traditional discrete-time recurrent neural networks by allowing for variable-length trajectories and efficient backpropagation through continuous time. The Neural ODE is trained on robot interaction data to learn a continuous state transition function, $f(x(t), u(t))$, where $x(t)$ is the robot’s state and $u(t)$ is the control input. This learned dynamic model facilitates both prediction of future states given current conditions and adaptation to changing environments or robot morphologies by re-training the Neural ODE with new data. The continuous nature of the model allows for accurate simulation and control even with complex, non-linear soft robot behavior.

The system’s operational safety is maintained through the integration of Control Barrier Functions (CBFs) within an Adaptive Safety Filter. CBFs define a safe set of states by constraining the system’s dynamics, ensuring that trajectories remain within pre-defined boundaries and avoid collisions or other hazardous conditions. The Adaptive Safety Filter dynamically adjusts the CBF constraints based on real-time state estimation and environmental feedback. This adaptation allows the system to maintain stability and safety even under uncertainties or disturbances, and facilitates operation closer to the boundaries of the safe set without violating safety criteria. The filter’s adaptability is achieved through online tuning of the CBF parameters, based on observed system behavior and environmental changes, allowing for robust and reliable safe operation.

Sampling-Based Model Predictive Control (MPC) is utilized within the framework to determine optimal control inputs by iteratively exploring a range of possible future trajectories. This approach involves repeatedly simulating the soft robot’s dynamics under various control sequences, evaluating each trajectory against defined cost functions and safety constraints. The MPC algorithm then selects the control input that minimizes the predicted cost while satisfying all constraints over a finite prediction horizon. Because calculating the optimal solution for complex, non-linear systems is computationally expensive, sampling-based methods are employed to efficiently approximate the solution space and identify feasible, high-performing control actions. The process is repeated at each time step, utilizing updated state information to re-plan and adapt to changing conditions, thereby enabling robust and efficient control of the soft robot.

Experimental validation demonstrates the framework’s robustness against model degradation, safety under actuator limits, and real-time performance in dynamic environments, consistently maintaining accurate and constraint-compliant control even during challenging maneuvers and with a speed of up to 0.32 m/s.
Experimental validation demonstrates the framework’s robustness against model degradation, safety under actuator limits, and real-time performance in dynamic environments, consistently maintaining accurate and constraint-compliant control even during challenging maneuvers and with a speed of up to 0.32 m/s.

Reciprocal Alignment: Mapping Prediction to Action

Reciprocal mapping functions as a bidirectional relationship between desired robot trajectories and the corresponding control signals necessary for their execution. This process involves predicting the outcome of specific control inputs and, conversely, determining the required inputs to achieve a predicted motion. By establishing this correspondence, the system can translate high-level behavioral goals – such as a desired path or speed – into concrete actuator commands. The technique is particularly relevant for robots with complex dynamics, where a direct mapping from control to motion is difficult to establish or maintain due to factors like actuator limitations or environmental disturbances. This allows the control framework to effectively account for the robot’s kinematic and dynamic properties when generating actions.

Soft robotic systems present unique control challenges due to their inherent compliance and the difficulties in modeling their continuous deformations. This framework addresses these complexities by leveraging reciprocal mapping, which effectively decouples motion prediction from low-level control. Traditional control methods often struggle with imprecise actuation, as small errors can compound and lead to significant deviations from the desired trajectory. Reciprocal mapping mitigates this issue by allowing the system to predict the consequences of various control inputs and select those that best achieve the predicted outcome, even with imperfect action representations. This approach bypasses the need for highly accurate forward models of the robot’s dynamics, enabling robust control despite the complexities of soft robot actuation and the inherent imprecision in controlling their many degrees of freedom.

Control Compliance, facilitated by reciprocal mapping, addresses the inherent challenges of controlling soft robots with imprecise action representations. This is achieved by establishing a functional relationship between predicted robot behaviors and the corresponding control signals, allowing the system to tolerate discrepancies between the intended action and its actual execution. Consequently, the framework maintains robust performance despite inaccuracies in action representations, enabling adaptable control strategies that can compensate for uncertainties in the robot’s physical properties or environmental conditions. This approach prioritizes achieving the desired outcome – such as maintaining a safe distance from obstacles – rather than strictly adhering to a predefined trajectory, improving overall system reliability.

Experimental results utilizing a robotic fish platform demonstrated successful obstacle avoidance capabilities. During testing, the robotic fish consistently maintained minimum distances of 0.33 meters and 0.16 meters from detected obstacles. These achieved distances exceeded the predefined safety threshold of 0.10 meters, indicating a robust capacity for navigating complex environments while adhering to specified safety parameters. This performance validates the effectiveness of the reciprocal mapping and control compliance techniques in a practical application.

Experimental validation demonstrates that a soft-bodied robotic fish can navigate a confined, obstacle-filled aquatic environment using a flexible tail and input modulation, maintaining safe distances as confirmed by multiple trials.
Experimental validation demonstrates that a soft-bodied robotic fish can navigate a confined, obstacle-filled aquatic environment using a flexible tail and input modulation, maintaining safe distances as confirmed by multiple trials.

Broadening the Horizon: Versatility Across Robotic Platforms

The newly developed control framework transcends the limitations of traditional robotics by demonstrating successful implementation across remarkably diverse platforms. Researchers validated its effectiveness not only on engineered systems like a Tendon-Driven Soft Arm – showcasing adaptability to complex, cable-driven mechanics – and a fully deformable Soft-Bodied Robotic Fish, but also extended its reach into the realm of bio-hybrid robotics with a Cyborg Cockroach. This integration with a living organism highlights the framework’s robustness and potential for nuanced control, regardless of the underlying actuation method or the robot’s physical morphology, paving the way for applications in previously inaccessible environments.

The core strength of this newly developed control framework lies in its remarkable adaptability, successfully interfacing with markedly different robotic systems. Beyond theoretical promise, the framework has proven its robustness by functioning effectively on platforms employing diverse actuation methods – from the fluid movements of a tendon-driven soft arm and the biomimicry of a soft-bodied robotic fish, to the unconventional integration with a cyborg cockroach. This isn’t simply about compatibility; the system intelligently adjusts to each platform’s unique morphology and operational characteristics, maintaining consistent performance regardless of whether the robot relies on traditional motors, pneumatic systems, or even biological muscle tissue. The ability to transcend specific hardware configurations positions this framework as a truly versatile solution, opening doors to broader implementation across the robotics landscape.

Recent experimentation utilizing a cyborg cockroach as a test subject revealed significant improvements in stability and precision when guided by the novel control framework. Under continuous stimulation, the cockroach exhibited demonstrably lower variance – a measure of erratic movement – and a smaller mean deviation from the intended path when compared to traditional control methods. This suggests the framework effectively mitigates the inherent challenges of biological locomotion, providing a more consistent and predictable steering response. The observed reduction in both variance and mean deviation highlights the framework’s potential for applications requiring delicate and accurate control of bio-hybrid robots, particularly in environments where consistent performance is paramount.

The framework’s demonstrated adaptability across markedly different robotic systems – from the delicate precision of soft-bodied manipulators to the bio-hybrid control of insect-scale robots – suggests a broad applicability extending beyond traditional robotics. This versatility unlocks potential in fields requiring nuanced and adaptable control, such as minimally invasive surgical procedures where dexterity within constrained spaces is paramount, and environmental monitoring where robust, bio-inspired robots can navigate complex terrains and collect data in challenging conditions. Furthermore, the framework’s capacity to integrate with diverse actuation methods and morphologies implies scalability, offering a pathway toward developing specialized robotic solutions tailored to a wide spectrum of applications, ultimately bridging the gap between theoretical control algorithms and real-world implementation.

Experimental validation demonstrates the soft robotic arm reliably tracks trajectories and avoids obstacles while maintaining safe operation, as evidenced by consistently positive tracking and avoidance safety functions across multiple trials.
Experimental validation demonstrates the soft robotic arm reliably tracks trajectories and avoids obstacles while maintaining safe operation, as evidenced by consistently positive tracking and avoidance safety functions across multiple trials.

The pursuit of robust control, as demonstrated in this work concerning soft robotic systems, echoes a fundamental truth about all complex systems. While the paper details a novel framework leveraging ‘control compliance’ to navigate imprecision, it implicitly acknowledges the inevitable decay inherent in any physical instantiation. As David Hilbert observed, “We must be able to answer the question: what are the ultimate parts of mathematics?” This translates directly to robotics: what are the ultimate, reliable components of control? The research subtly suggests that accepting a degree of controlled imprecision-acknowledging latency as a necessary tax-is not a limitation, but a pathway to graceful aging for these systems, allowing them to function effectively despite the inevitable erosion of perfect fidelity over time. The focus on generalizability isn’t about achieving perfection, but building a system resilient enough to operate within the bounds of imperfection.

What Remains to Be Seen

The pursuit of generic control, as demonstrated by this work, inevitably encounters the limitations inherent in any attempt to impose order on fundamentally compliant systems. Every failure is a signal from time; the tolerance for imprecise action, while advantageous, merely delays the inevitable decay of predictive fidelity. The framework’s success across diverse platforms suggests a certain universality, but this is a provisional observation. The true test lies not in achieving control, but in gracefully accommodating its loss.

Future work will likely focus on refining the interplay between model fidelity and control compliance. Sampling-based methods, while effective, demand computational resources. The question is not simply ‘more data,’ but ‘more insightful abstraction.’ Refactoring is a dialogue with the past; the system must learn to anticipate not just the present state, but the modes of its own degradation. Bio-hybrid robots, with their inherent biological variability, present a particularly poignant challenge-a reminder that control is never absolute, only a temporary negotiation with entropy.

Ultimately, the longevity of this approach, and indeed the field of soft robotics itself, will depend on acknowledging the transient nature of control. Safety-guaranteed control is not a destination, but a continuous recalibration-an acceptance that the most robust systems are those that anticipate, and even embrace, their own obsolescence.

Original article: https://arxiv.org/pdf/2511.20226.pdf

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

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2025-11-26 07:21