This paper addresses the task of modeling Deformable Linear Objects (DLOs), such as ropes and cables, during dynamic motion over long time horizons. This task presents significant challenges due to the complex dynamics of DLOs. To address these challenges, this paper proposes differentiable Discrete Elastic Rods For deformable linear Objects with Real-time Modeling (DEFORM), a novel framework that combines a differentiable physics-based model with a learning framework to model DLOs accurately and in real-time. The performance of DEFORM is evaluated in an experimental setup involving two industrial robots and a variety of sensors. A comprehensive series of experiments demonstrate the efficacy of DEFORM in terms of accuracy, computational speed, and generalizability when compared to state-of-the-art alternatives. To further demonstrate the utility of DEFORM, this paper integrates it into a perception pipeline and illustrates its superior performance when compared to the state-of-the-art methods while tracking a DLO even in the presence of occlusions. Finally, this paper illustrates the superior performance of DEFORM when compared to state-of-the-art methods when it is applied to perform autonomous planning and control of DLOs. Project page: https://roahmlab.github.io/DEFORM/.

Two combinatorial mechanical metamaterials designed in such a way that the letters M and L bulge out in the front when being squeezed between two plates (top and bottom). Designing novel metamaterials such as this can be aided by machine learning. Given a 3D piece of origami, can you flatten it without damaging it? Just by looking at the design, the answer is hard to predict, because each and every fold in the design has to be compatible with flattening. This is an example of a combinatorial problem.

Given a 3D piece of origami, can you flatten it without damaging it? Just by looking at the design, the answer is hard to predict, because each and every fold in the design has to be compatible with flattening. This is an example of a combinatorial problem. New research led by the UvA Institute of Physics and research institute AMOLF has demonstrated that machine learning algorithms can accurately and efficiently answer these kinds of questions. This is expected to give a boost to the artificial intelligence-assisted design of complex and functional (meta)materials.

Tool use has long been a hallmark of human intelligence, as well as a practical problem to solve for a vast array of robotic applications. But machines are still wonky at exerting just the right amount of force to control tools that aren't rigidly attached to their hands. To manipulate said tools more robustly, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with the Toyota Research Institute (TRI), have designed a system that can grasp tools and apply the appropriate amount of force for a given task, like squeegeeing up liquid or writing out a word with a pen. The system, dubbed Series Elastic End Effectors, or SEED, uses soft bubble grippers and embedded cameras to map how the grippers deform over a six-dimensional space (think of an airbag inflating and deflating) and apply force to a tool. Using six degrees of freedom, the object can be moved left and right, up or down, back and forth, roll, pitch, and yaw. The closed-loop controller--a self-regulating system that maintains a desired state without human interaction--uses SEED and visuotactile feedback to adjust the position of the robot arm in order to apply the desired force.

Tool use has long been a hallmark of human intelligence, as well as a practical problem to solve for a vast array of robotic applications. But machines are still wonky at exerting just the right amount of force to control tools that aren't rigidly attached to their hands. To manipulate said tools more robustly, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with the Toyota Research Institute (TRI), have designed a system that can grasp tools and apply the appropriate amount of force for a given task, like squeegeeing up liquid or writing out a word with a pen. The system, dubbed Series Elastic End Effectors, or SEED, uses soft bubble grippers and embedded cameras to map how the grippers deform over a six-dimensional space (think of an airbag inflating and deflating) and apply force to a tool. Using six degrees of freedom, the object can be moved left and right, up or down, back and forth, roll, pitch, and yaw.

There are some tasks that traditional robots -- the rigid and metallic kind -- simply aren't cut out for. Soft-bodied robots, on the other hand, may be able to interact with people more safely or slip into tight spaces with ease. But for robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot's body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design.

During the swarming of birds or fish, each entity coordinates its location relative to the others, so that the swarm moves as one larger, coherent unit. Fireflies on the other hand coordinate their temporal behavior: within a group, they eventually all flash on and off at the same time and thus act as synchronized oscillators. Few entities, however, coordinate both their spatial movements and inherent time clocks; the limited examples are termed "swarmalators", which simultaneously swarm in space and oscillate in time. Japanese tree frogs are exemplar swarmalators: each frog changes both its location and rate of croaking relative to all the other frogs in a group. Moreover, the frogs change shape when they croak: the air sac below their mouth inflates and deflates to make the sound. This coordinated behavior plays an important role during mating and hence, is vital to the frogs' survival.

This story originally appeared in the December issue of Discover magazine as "Scientist in Toyland." It's easy to pin labels on Chuck Hoberman, but hard to stick with just one. He's a toymaker -- the brains behind the colorful, expanding Hoberman sphere, which you and your kids have been playing with since the early 1990s (and which earned a place in the Museum of Modern Art's permanent collection). Physically, he works sometimes from an airy room on the second floor of a house-turned-office-suite near Harvard Square in Cambridge, Massachusetts. The Cambridge office is tidy, with white walls and plenty of light. The surfaces are usually cleared, but today they're cluttered with the material expressions of his geometric dreams: Models made of two-dimensional pieces, hinged together to form 3D structures that deform, bend or otherwise fold in prescribed ways.

A group of scientists have created a resilient RoboBee, that can survive crashing into walls and other robots without being damaged. The invention marks the first microrobot powered by soft artificial muscles that has achieved a controlled flight. Researchers in the Harvard Microrobotics Laboratory at the Harvard John A. Paulson School of Engineering and Applied Science (SEAS) developed a resilient artificial bee powered by soft actuators. Often these soft components have been dismissed as too difficult to control as their flexibility can lead to the system buckling at weak points if pushed to activate movements at speed. Yufeng Chen, a former graduate student and postdoctoral fellow at SEAS and first author of the paper, said: 'There has been a big push in the field of microrobotics to make mobile robots out of soft actuators because they are so resilient.'

A synthetic mesh could give robots a sense of touch that is delicate as the skin on out backs, researchers have claimed. The material forms a linked sensory network similar to that of a biological nervous system -- one that could help robots feel their interactions with the environment. The lattice is made of flexible polyurethane that contains stretchable optical fibres with sensors than can detect how the fibres are being deformed. The device -- a sort-of stretchable optical lace -- was developed by roboticists Patricia Xu and Rob Shepherd of Cornell University and colleagues. 'We want to have a way to measure stresses and strains for highly deformable objects, and we want to do it using the hardware itself, not vision,' said Professor Shepherd.