Soft robotics has advanced rapidly, yet its control methods remain fragmented: different morphologies and actuation schemes still require task-specific controllers, hindering theoretical integration and large-scale deployment. A generic control framework is therefore essential, and a key obstacle lies in the persistent use of rigid-body control logic, which relies on precise models and strict low-level execution. Such a paradigm is effective for rigid robots but fails for soft robots, where the ability to tolerate and exploit approximate action representations, i.e., control compliance, is the basis of robustness and adaptability rather than a disturbance to be eliminated. Control should thus shift from suppressing compliance to explicitly exploiting it. Human motor control exemplifies this principle: instead of computing exact dynamics or issuing detailed muscle-level commands, it expresses intention through high-level movement tendencies, while reflexes and biomechanical mechanisms autonomously resolve local details. This architecture enables robustness, flexibility, and cross-task generalization. Motivated by this insight, we propose a generic soft-robot control framework grounded in control compliance and validate it across robots with diverse morphologies and actuation mechanisms. The results demonstrate stable, safe, and cross-platform transferable behavior, indicating that embracing control compliance, rather than resisting it, may provide a widely applicable foundation for unified soft-robot control.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Fairly recently, I started being verbally abusive to large language models. I highly recommend you experiment with doing so yourself. Over the past 30 days, I have called large language models (primarily OpenAI's paid product) the following names, among others that I won't repeat here because my mom might read this: Dipshit, fucknuts, shitstain, dummy, dumbass, dum-dum fucking dumbass dum-dum, numbnuts, hockey puck (thank you, Don Rickles), turdburger, lickspittle, cockroach, fucking cockroach (thank you, Tony Montana), idiot, fucking idiot, total fucking idiot, and fucking numbnuts dipshit. Ethan Mollick, author of Co-Intelligence: Living and Working With AI, and currently the reigning A.I. whisperer for the consultant class, says that anthropomorphizing A.I. is "a sin of necessity."

A robotic arm that can automatically turn cockroaches into controllable cyborgs could be used to create swarms of biological robots for search missions. Hirotaka Sato at Nanyang Technological University in Singapore and his colleagues have previously shown that groups of up to 20 Madagascar hissing cockroaches (Gromphadorhina portentosa) equipped with electronic backpacks can be steered across desert-like terrain. However, to be used in a real-world search-and-rescue mission, the team calculates that hundreds or thousands of cyborg insects would be needed.

The advancement of insect-computer hybrid robots holds significant promise for navigating complex terrains and enhancing robotics applications. This study introduced an automatic assembly method for insect-computer hybrid robots, which was accomplished by mounting backpack with precise implantation of custom-designed bipolar electrodes. We developed a stimulation protocol for the intersegmental membrane between pronotum and mesothorax of the Madagascar hissing cockroach, allowing for bipolar electrodes' automatic implantation using a robotic arm. The assembly process was integrated with a deep learning-based vision system to accurately identify the implantation site, and a dedicated structure to fix the insect (68 s for the whole assembly process). The automatically assembled hybrid robots demonstrated steering control (over 70 degrees for 0.4 s stimulation) and deceleration control (68.2% speed reduction for 0.4 s stimulation), matching the performance of manually assembled systems. Furthermore, a multi-agent system consisting of 4 hybrid robots successfully covered obstructed outdoor terrain (80.25% for 10 minutes 31 seconds), highlighting the feasibility of mass-producing these systems for practical applications. The proposed automatic assembly strategy reduced preparation time for the insect-computer hybrid robots while maintaining their precise control, laying a foundation for scalable production and deployment in real-world applications.

The centimeter-scale cyborg insects have a potential advantage for application in narrow environments where humans cannot operate. To realize such tasks, researchers have developed a small printed-circuit-board (PCB) which an insect can carry and control it. The electronic components usually remain bare on the board and the whole board is mounted on platform animals, resulting in uneven morphology of whole cyborg with sharp edges. It is well known that streamlined body shape in artificial vehicles or robots contributes to effective locomotion by reducing drag force in media. However, little is known how the entire body shape impacts on locomotor performance of cyborg insect. Here, we developed a 10 mm by 10 mm board which provided electrical stimulation via Sub-GHz communication and investigated the impact of physical arrangement of the board using Madagascar hissing cockroach. We compared the success rate of gap negotiation between the cyborg with mounted board and implanted board and found the latter outperformed the former. We demonstrated our cyborg cockroach with implanted board could follow faithfully to the locomotion command via antennal or cercal stimulation and traverse a narrow gap like air vent cover. In contrast to the conventional arrangement, our cyborg insects are suitable for application in a concealed environment.

Have you ever wondered why some insects like cockroaches prefer to stay or decrease movement in darkness? Some may tell you it's called photophobia, a habit deeply coded in their genes. A further question would be whether we can correct this habit of cockroaches, that is, moving in the darkness just as they move in bright backgrounds. Scientists from Osaka University may have answered this question by converting a cockroach into a cyborg. They published their research in the journal Cyborg and Bionic Systems.

Many applications require robots to move through complex 3-D terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered large obstacles. To make progress, we need to better understand how to use and control the physical interaction with obstacles to traverse them. Forest floor-dwelling cockroaches can use physical interaction to transition between different locomotor modes to traverse flexible, grass-like beams of a large range of stiffness. Inspired by this, here we studied whether and how environmental force sensing helps robots make active adjustments to traverse cluttered large obstacles. We developed a physics model and a simulation of a minimalistic robot capable of sensing environmental forces during traversal of beam obstacles. Then, we developed a force-feedback control strategy, which estimated beam stiffness from the sensed contact force using the physics model. Then in simulation we used the estimated stiffness to control the robot to either stay in or transition to the more favorable locomotor modes to traverse. When beams were stiff, force sensing induced the robot to transition from a more costly pitch mode to a less costly roll mode, which helped the robot traverse with a higher success rate and less energy consumed. By contrast, if the robot simply pushed forward or always avoided obstacles, it would consume more energy, become stuck in front of beams, or even flip over. When the beams were flimsy, force sensing guided the robot to simply push across the beams. In addition, we demonstrated the robustness of beam stiffness estimation against body oscillations, randomness in oscillation, and uncertainty in position sensing. We also found that a shorter sensorimotor delay reduced energy cost of traversal.

A dish of living brain cells has learned to play the 1970s arcade game Pong. About 800,000 cells linked to a computer gradually learned to sense the position of the game's electronic ball and control a virtual paddle, a team reports in the journal Neuron. The novel achievement is part of an effort to understand how the brain learns, and how to make computers more intelligent. "We've made huge strides with silicon computing, but they're still rigid and inflexible," says Brett Kagan, an author of the study and chief scientific officer at Cortical Labs in Melbourne, Australia. "That's something we don't see with biology." For example, both computers and people can learn to make a cup of tea, Kagan says.

A new study found that all of these kinds of motion are well represented by a single mathematical model. "This didn't come out of nowhere -- this is from our real robot data," said Dan Zhao, first author of the study in the Proceedings of the National Academy of Sciences and a recent Ph.D. graduate in mechanical engineering at the University of Michigan. "Even when the robot looks like it's sliding, like its feet are slipping, its velocity is still proportional to how quickly it's moving its body." Unlike the dynamic motion of gliding birds and sharks and galloping horses -- where speed is driven, at least in part, by momentum -- every bit of speed for ants, centipedes, snakes and swimming microbes is driven by changing the shape of the body. This is known as kinematic motion.

On a beautiful day in May 2015, I drove the 13 hours from my home in Portland, Oregon, to Harris Ranch, California, halfway between San Francisco and Los Angeles. At the time, Tesla was touting a battery-swap station that could send Tesla drivers on their way in a fully powered vehicle in less than the time it takes to fill up a car with gas. Overtaken by curiosity, I had decided to spend a long Memorial Day weekend in California's Central Valley to see if Elon Musk's latest bit of dream weaving could stand up to reality. There, amid the pervasive stench of cow droppings from a nearby feedlot, I discovered that Tesla's battery swap station was not in fact being made available to owners who regularly drove between California's two largest cities. Instead, the company was running diesel generators to power additional Superchargers (the kind that take 30 to 60 minutes to recharge a battery) to handle the holiday rush, their exhaust mingling with the unmistakable smell of bullshit.