Robotics

Robotic navigation on land, through air, and in water is well researched; numerous robots have successfully demonstrated motion in these environments. However, one frontier for robotic locomotion remains largely unexplored—below ground. Subterranean navigation is simply hard to do, in part because the interaction forces of underground motion are higher than in air or water by orders of magnitude and because we lack for these interactions a robust fundamental physics understanding. We present and test three hypotheses, derived from biological observation and the physics of granular intrusion, and use the results to inform the design of our burrowing robot. These results…

A bioinspired soft robot burrows through shallow dry sand with remarkable speed and maneuverability. Source: Science Mag: Burrowing soft robots break new ground

Science fiction was prescient about many aspects of grasping and manipulation, but can it keep up with new advances? Source: Science Mag: Robots have grasped and manipulated the imagination since 1839

The ability to reliably grasp and manipulate novel objects is a grand challenge for robotics. Source: Science Mag: Getting a grip on reality

Tactile feedback is a natural pathway to robot dexterity in unstructured settings. Source: Science Mag: The unstable queen: Uncertainty, mechanics, and tactile feedback

The world outside our laboratories seldom conforms to the assumptions of our models. This is especially true for dynamics models used in control and motion planning for complex high–degree of freedom systems like deformable objects. We must develop better models, but we must also consider that, no matter how powerful our simulators or how big our datasets, our models will sometimes be wrong. What is more, estimating how wrong models are can be difficult, because methods that predict uncertainty distributions based on training data do not account for unseen scenarios. To deploy robots in unstructured environments, we must address two…

The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand’s actuation input to the object’s motion to be obtained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic…

Scifi assumes creating a robot mother will be easy, research indicates otherwise, but both suggest you might not want one anyway. Source: Science Mag: Robot mothers in science fiction

Humans use all surfaces of the hand for contact-rich manipulation. Robot hands, in contrast, typically use only the fingertips, which can limit dexterity. In this work, we leveraged a potential energy–based whole-hand manipulation model, which does not depend on contact wrench modeling like traditional approaches, to design a robotic manipulator. Inspired by robotic caging grasps and the high levels of dexterity observed in human manipulation, a metric was developed and used in conjunction with the manipulation model to design a two-fingered dexterous hand, the Model W. This was accomplished by simulating all planar finger topologies composed of open kinematic chains…

The ability to grab, hold, and manipulate objects is a vital and fundamental operation in biological and engineering systems. Here, we present a soft gripper using a simple material system that enables precise and rapid grasping, and can be miniaturized, modularized, and remotely actuated. This soft gripper is based on kirigami shells—thin, elastic shells patterned with an array of cuts. The kirigami cut pattern is determined by evaluating the shell’s mechanics and geometry, using a combination of experiments, finite element simulations, and theoretical modeling, which enables the gripper design to be both scalable and material independent. We demonstrate that the…

Policy gradient methods can be used for mechanical and computational co-design of robot manipulators. Source: Science Mag: Co-designing hardware and control for robot hands

A watchmaker’s approach yields small, agile, soft machines. Source: Science Mag: The jig is up for small soft machines