Jeffrey Lipton

Victoria A. Webster-Wood, Nicholas Gravish, Amir Alavi et al.

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

Sawyer Thomas, Jeffrey Lipton

The ability to change a surface's profile allows biological systems to effectively manipulate and blend into their surroundings. Current surface morphing techniques rely either on having a small number of fixed states or on directly driving the entire system. We discovered a subset of scale-independent auxetic metamaterials have a state trajectory with a star-graph structure. At the central node, small nudges can move the material between trajectories, allowing us to locally shift Poisson's ratio, causing the material to take on different shapes under loading. While the number of possible shapes grows exponentially with the size of the material, the probability of finding one at random is vanishingly small. By actively guiding the material through the node points, we produce a reprogrammable surface that does not require inputs to maintain shape and can display arbitrary 2D information and take on complex 3D shapes. Our work opens new opportunities in micro devices, tactile displays, manufacturing, and robotic systems.

Aman Garg, Ian Good, Daniel Revier et al.

Handed Shearing Auxetics (HSA) are a promising technique for making motor-driven, soft, continuum robots. Many potential applications from inspection tasks to solar tracking require accurate kinematic models to predict the position and orientation of these structures. Currently there are no models for HSA based continuum platforms. To address this gap we propose to adapt Piecewise Constant Curvature (PCC) Models using a length change coupling matrix. This models the interaction of HSA structures in a 2x2 array. The coupling matrix maps the change in motor angles to length changes and defines the configuration space in our modified PCC Model. We evaluate our model on a composite movement encompassing bending, extension and compression behavior. Our model achieves a positional accuracy with mean error of 5.5mm or 4.5% body length and standard deviation of 1.72mm. Further, we achieve an angular accuracy with mean error of -2.8$^\circ$ and standard deviation of 1.9$^\circ$.