Desert ants are particularly excellent navigators, able to use visual memories to learn long foraging routes and return to their nest in complex natural environments. The mushroom body (MB) is believed to be the key brain region that forms these visual memories. Specifically, the MBs can store two types of parallel and opposing valence memories, treated as attractive and repulsive, respectively. However, the underlying neural computations for integrating two opposing visual memories remain unclear. Based on biological findings, we propose two possible integration mechanisms for these opposing visual memories and implement both in our model. The first method, Subtraction-Based Integration, calculates the familiarity of the view in the current direction by computing the difference in the firing rates of two mushroom body output neurons (MBONs), which then guides directional decisions. The second method, Vector-Sum Integration, computes a preferred direction for each MBON, with the final movement direction determined by summing these directional vectors. We validated the effectiveness and testability of both integration methods by replicating recent biological experiments. Results demonstrates that both models could successfully replicate real ant’s trap-avoiding behavior, offering insights into the mechanisms by which insects could combine appetitive and aversive memories to make appropriate navigational decision.
Animals can accomplish many incredible behavioral feats across a wide range of operational environments and scales that current robots struggle to match. One explanation for this performance gap is the extraordinary properties of the biological materials that comprise animals, such as muscle tissue. Using living muscle tissue as an actuator can endow robotic systems with highly desirable properties such as self-healing, compliance, and biocompatibility. Unlike traditional soft robotic actuators, living muscle biohybrid actuators exhibit unique adaptability, growing stronger with use. While muscle adaptability is a benefit to muscular organisms, it currently presents a challenge for biohybrid researchers: how does one design and control a robot whose actuators’ force output changes over time? Here, we incorporate muscle adaptability into a many-muscle biohybrid robot design and modeling tool, leveraging reinforcement learning as both a co-design partner and system controller. Our results show that adaptive agents outperform non-adaptive agents in terms of maximum rewards and training time. Together, these contributions can enable the elucidation of muscle actuator adaptation and inform the design and modeling of adaptive many-muscle robots.
Recent advancements in rapid prototyping technologies, such as 3D printing, have transformed industries by enabling fast and customizable fabrication. Meanwhile, the ancient art of origami—where complex three-dimensional structures emerge from folding flat sheets—offers a unique, reversible, and geometrically efficient alternative, increasingly explored in fields like robotics and deployable systems. While self-folding origami has shown promise in micro-scale applications, challenges such as torque limitations and hinge friction impede its scalability. To explore how programmable origami mechanisms can be physically realized and publicly experienced at scale, this study presents a large-scale transforming kinetic sculpture designed for an interactive exhibition context. The system employs a tendon-driven actuation mechanism and is fabricated from laser-cut acrylic slices and double-layer PET living hinges, enabling smooth expansion from 0.7 m to 3.5 m in height. The sculpture features both an automated display section and a manually operated audience interaction section, encouraging tactile engagement with structural transformation. During a week-long public exhibition, the installation operated continuously for over 7 h per day and completed an average of 46 actuation cycles daily without failure. This work demonstrates the mechanical robustness and scalability of tendon-actuated origami structures and highlights the potential of kinetic origami as an accessible and programmable medium for public engagement, bridging technical innovation with artistic expression.
This paper presents the design and development of a biologically inspired rat robot. The robot is scaled 2.5 times the size of a female Sprague-Dawley rat. The hindlimbs are each equipped with 4 motors to control the hip, knee, and ankle rotation in the sagittal plane, and internal/external hip rotation. The forelimbs are equipped with five motors to control the scapula, shoulder, elbow, and wrist rotation in the sagittal plane, as well as abduction/adduction of the scapula. Additionally, the hands and feet of the robot are comprised of two sections, connected with a pin and torsional springs. This allows the feet to have passive compliance and conform to the ground while walking. The leg segments are based on scanned rat bones with shapes modified for ease of assembly and 3D printing. Parts are printed using PLA with internal supports for structural reinforcement. The scapula, shoulder, and hip joints are directly driven by motors. The lower joints are driven by motors using pulley-belt transmission systems to allow the motors to be mounted more proximal, reducing the legs’ inertia. This robot was developed as a physical platform to test and make predictions about how the animal and its nervous system may interact with the environment in a more realistic way than in software simulations alone.
Swallowable robots offer a promising frontier for biomedical applications such as targeted drug delivery or sampling within the gastrointestinal tract. This work presents a compact, magnetically actuated crank-slider mechanism fully embedded in a size 000 pharmaceutical capsule (length:25.8 mm, diamater:9.9 mm) that is capable of (1) targeted delivery of substances (sucrose) and (2) mechanical sampling or injection via controlled needle deployment. Both actions are validated through proof-of-concept tests. The device features a permanent magnet attached to a 3D-printed crank-slider system, actuated externally by four electromagnetic coils. This magnetic control strategy allows for the development of compact, electronics-free, and more biocompatible robotic systems. This study presents a fully realized, low-cost, functional prototype that can be scaled and adapted for different applications. The system acts as a programmable, wireless mini-actuator operating without onboard power. Its simplicity, modularity, and remote activation make it relevant to biomimetic devices, and intelligent prosthetic tools for biomedical use.
