Picture a flexible, continuous robotic arm curving around a cluster of grapes or a head of broccoli, modifying its grasp instantly as it picks up the item. In contrast to conventional, unyielding robots that typically try to minimize environmental contact and maintain a safe distance from people, this arm detects minute forces, extending and bending in a manner similar to the adaptability of a human hand. Each movement is carefully planned to prevent applying too much force while still completing the job effectively.
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At MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Laboratory for Information and Decisions Systems (LIDS), these actions that appear straightforward are actually the result of intricate mathematical models, meticulous engineering, and a forward-thinking approach to robotics that prioritizes safe interaction between robots, people, and fragile items.
Soft-bodied robots, owing to their pliable forms, suggest a future where machines interact more naturally with humans, aid in providing care, or manipulate fragile objects in manufacturing environments. However, this inherent suppleness presents control challenges. Minor flexes or contortions can generate erratic forces, increasing the potential for harm or destruction. This underscores the importance of developing secure control methods for soft robots.
Gioele Zardini, the primary senior author, MIT Assistant Professor, principal investigator at LIDS and the Department of Civil and Environmental Engineering, and affiliated faculty member with IDSS, explains that they are applying concepts from safe control and formal methods—previously used for rigid robots—to the field of soft robotics. The goal is to create better designs that can handle larger loads and be more precise, while maintaining safety and inherent intelligence, by both modeling the intricate behavior of soft robots and actively utilizing, rather than circumventing, contact.
Other research teams have come to similar conclusions independently.
The paper is published in the journal IEEE Robotics and Automation Letters.
Safety first
The team created a novel framework by combining nonlinear control theory (which governs systems with intricate dynamics) with sophisticated physical modeling methods and rapid, real-time optimization, resulting in their so-called “contact-aware safety.”
The core of this method relies on high-order control barrier functions (HOCBFs) and high-order control Lyapunov functions (HOCLFs). HOCBFs establish secure operational limits, guaranteeing the robot avoids applying dangerous levels of force. HOCLFs direct the robot effectively towards achieving its goals, striking a balance between safety and optimal execution.
“In essence, we are training the robot to understand its capabilities while engaging with its surroundings, all while ensuring it meets its objectives,” explains Kiwan Wong, a Ph.D. candidate in MIT’s Department of Mechanical Engineering and primary writer of a recent publication detailing the system.
This method includes some intricate calculations derived from…soft robot dynamicsWhile it involves contact models and control constraints, setting up control objectives and safety barriers is relatively simple for users. The results are clear: you can observe the robot moving fluidly, responding to touch, and avoiding dangerous scenarios.
“Compared with traditional kinematic CBFs—where forward-invariant safe sets are hard to specify—the HOCBF framework simplifies barrier design, and its optimization formulation accounts for system dynamics (e.g., inertia), ensuring the soft robot stops early enough to avoid unsafe contact forces,” says Worcester Polytechnic Institute Assistant Professor and former CSAIL postdoc Wei Xiao.
“Since soft robots emerged, the field has highlighted their embodied intelligence and greater inherent safety relative to rigid robots, thanks to passive material and structural compliance.
Yet their “cognitive” intelligence—especially safety systems—has lagged behind that of rigid serial-link manipulators,” says co-lead author Maximilian Stölzle, a research intern at Disney Research and formerly a Delft University of Technology Ph.D. student and visiting researcher at MIT LIDS and CSAIL.
“This work helps close that gap by adapting proven algorithms to soft robots and tailoring them for safe contact and soft-continuum dynamics.”
The LIDS and CSAIL team tested the system on a series of experiments designed to challenge the robot’s safety and adaptability. In one test, the arm pressed gently against a compliant surface, maintaining a precise force without overshooting. In another, it traced the contours of a curved object, adjusting its grip to avoid slippage.
In yet another demonstration, the robot manipulated fragile items alongside a human operator, reacting in real time to unexpected nudges or shifts.
“These experiments show that our framework is able to generalize to diverse tasks and objectives, and the robot can sense, adapt, and act in complex scenarios while always respecting clearly defined safety limits,” says Zardini.
Soft robots with contact-aware safety could be a real value-add in high-stakes places, of course. In health care, they could assist in surgeries, providing precise manipulation while reducing risk to patients.
In industry, they might handle fragile goods without constant supervision. In domestic settings, robots could help with chores or caregiving tasks, interacting safely with children or the elderly—a key step toward making soft robots reliable partners in real-world environments.
“Soft robots have incredible potential,” says co-lead senior author Daniela Rus, director of CSAIL and a professor in the Department of Electrical Engineering and Computer Science.
“But ensuring safety and encoding motion tasks via relatively simple objectives has always been a central challenge. We wanted to create a system where the robot can remain flexible and responsive while mathematically guaranteeing it won’t exceed safe force limits.”
Combining soft robot models, differentiable simulation, and control theory
Underlying the control strategy is a differentiable implementation of something called the Piecewise Cosserat-Segment (PCS) dynamics model, which predicts how a soft robot deforms and where forces accumulate. This model allows the system to anticipate how the robot’s body will respond to actuation and complex interactions with the environment.
“The aspect that I most like about this work is the blend of integration of new and old tools coming from different fields, like advanced soft robot models, differentiable simulation, Lyapunov theory, convex optimization, and injury-severity–based safety constraints. All of this is nicely blended into a real-time controller fully grounded in first principles,” says co-author Cosimo Della Santina, who is an associate professor at Delft University of Technology.
Complementing this is the Differentiable Conservative Separating Axis Theorem (DCSAT), which estimates distances between the soft robot and obstacles in the environment that can be approximated with a chain of convex polygons in a differentiable manner.
“Earlier differentiable distance metrics for convex polygons either couldn’t compute penetration depth—essential for estimating contact forces—or yielded non-conservative estimates that could compromise safety,” says Wong.
“Instead, the DCSAT metric returns strictly conservative, and therefore safe, estimates while simultaneously allowing for fast and differentiable computation.” Together, PCS and DCSAT give the robot a predictive sense of its environment for more proactive, safe interactions.
Looking ahead, the team plans to extend their methods to three-dimensional soft robots and explore integration with learning-based strategies. By combining contact-aware safety with adaptive learning, soft robots could handle even more complex, unpredictable environments.
“This is what makes our work exciting,” says Rus. “You can see the robot behaving in a human-like, careful manner, but behind that grace is a rigorous control framework ensuring it never oversteps its bounds.”
“Soft robots are generally safer to interact with than rigid-bodied robots by design, due to the compliance and energy-absorbing properties of their bodies,” says University of Michigan Assistant Professor Daniel Bruder, who wasn’t involved in the research.
“However, as soft robots become faster, stronger, and more capable, that may no longer be enough to ensure safety. This work takes a crucial step towards ensuring soft robots can operate safely by offering a method to limit contact forces across their entire bodies.”
More information: Kiwan Wong et al, Contact-Aware Safety in Soft Robots Using High-Order Control Barrier and Lyapunov Functions, IEEE Robotics and Automation Letters (2025). DOI: 10.1109/lra.2025.3621965
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This story was originally published on Tech Xplore.
