Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses challenges as it involves direct physical contact between the human and the robot, and necessitates simple, safe, and effective control. In this paper, we introduce a soft, expandable robotic manipulator with embedded capacitive proximity sensing arrays, designed for safe and efficient bed bathing assistance. We conduct a thorough evaluation of our soft manipulator, comparing it with a baseline rigid end effector in a human study involving 12 participants across 96 bathing trails. Our soft manipulator achieves an an average cleaning effectiveness of 88.8% on arms and 81.4% on legs, far exceeding the performance of the baseline. Participant feedback further validates the manipulator’s ability to maintain safety, comfort, and thorough cleaning. https://sites.google.com/view/softbathing.

Small and medium-sized enterprises (SMEs) often struggle with automating high-mix, low-volume (HMLV) manufacturing due to the inflexibility and high cost of traditional automation solutions. This paper presents a novel approach to robotic manipulation for HMLV environments that leverages vibrotactile sensing. We propose integrating vibrotactile sensors, which capture subtle vibrations and acoustic signals, to provide real-time feedback during manipulation tasks. This approach enables the robot to detect subtle misalignments, which can assist in refining vision-based policies and improving the robot’s overall manipulation skills. We demonstrate the effectiveness of this method in several representative insertion tasks, showing how vibrotactile feedback can be used to predict success or failure of an insertion task as well as predict initial contact between an object grasped in-hand and the placement location. Our results suggest that vibrotactile sensing offers a promising pathway towards more robust and adaptable robotic systems that can better empower SMEs to embrace automation.

Springtails are tiny arthropods that crawl and jump. They jump by temporarily storing elastic energy in resilin elastic cuticular structures and releasing that energy to accelerate a tail, called a furca, propelling them in the air. This paper presents an autonomous, springtail-inspired microrobot that can crawl and jump. The microrobot has a mass of 980mg and stands 13mm tall, and has on-board sensing, computation, and power, enabling autonomy. The microrobot was designed with a super-elastic shape memory alloy (SMA) spring that is manually loaded to store elastic energy. The on-board sensing and computation triggers an actuator at the jump frequency range that unlatches the spring, launching the microrobot into the air at speeds up to 3.171ms−1. At the same time, the microrobot is capable of crawling, when actuated at frequencies lower or higher than the jump frequency range, demonstrating autonomous multi-modal locomotion. This work opens up new pathways toward autonomy in multi-modal microrobots.

This paper presents a new type of distributed dexterous manipulator: delta arrays. Our delta array setup consists of 64 linearly-actuated delta robots with 3D-printed compliant linkages. Through the design of the individual delta robots, the modular array structure, and distributed communication and control, we study a wide range of in-plane and out-of-plane manipulations, as well as prehensile manipulations among subsets of neighboring delta robots. We also demonstrate dexterous manipulation capabilities of the delta array using reinforcement learning while leveraging compliance. Our evaluations show that the resulting 192 DoF compliant robot is capable of performing various coordinated distributed manipulations of a variety of objects, including translation, alignment, prehensile squeezing, lifting, and grasping.

In this paper, we present a heterogeneous robot swarm system that can physically couple with each other to form functional structures and dynamically decouple to perform individual tasks. The connection between robots can be formed with a passive coupling mechanism, ensuring minimum energy consumption during coupling and decoupling behavior. The heterogeneity of the system enables the robots to perform structural enhancement configurations based on specific environmental requirements. We propose a connection-pair oriented configuration control algorithm to form different assemblies. We show experiments of up to nine robots performing the coupling, gap-crossing, and decoupling behaviors.

This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. The DeltaZ robot is 3D-printed from soft and rigid materials with a design that is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to many other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for research and education settings. We describe how the robot can be used as a reinforcement learning bench-mark. Open-source 3D-printable designs and code for building and using the robot are available to the public.

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a tactile sensor (ReSkin) to one of the two fingertips of a Franka robot and train a classifier to determine whether the robot is grasping a specific number of cloth layers. During test-time experiments, the robot uses this classifier as part of its policy to grasp one or two cloth layers using tactile feedback to determine suitable grasping points. Experimental results over 180 physical trials suggest that the proposed method outperforms baselines that do not use tactile feedback and has better generalization to unseen cloth compared to methods that use image classifiers. Code, data, and videos are available at https://sites.google.com/view/reskin-cloth.

Distributed manipulators - consisting of a set of actuators or robots working cooperatively to achieve a manipulation task - are robust and flexible tools for performing a range of planar manipulation skills. One novel example is the delta array, a distributed manipulator composed of a grid of delta robots, capable of performing dexterous manipulation tasks using strategies incorporating both dynamic and static contact. Hand-designing effective distributed control policies for such a manipulator can be complex and time consuming, given the high-dimensional action space and unfamiliar system dynamics. In this paper, we examine the principles guiding development and control of such a delta array for a planar translation task. We explore policy learning as a robust cooperative control approach, allowing for smooth manipulation of a range of objects, showing improved accuracy and efficiency over baseline human-designed policies.

Robot swarms have been shown to improve the ability of individual robots by inter-robot collaboration. In this paper, we present the PuzzleBots - a low-cost robotic swarm system where robots can physically couple with each other to form functional structures with minimum energy consumption while maintaining individual mobility to navigate within the environment. Each robot has knobs and holes along the sides of its body so that the robots can couple by inserting the knobs into the holes. We present the characterization of knob design and the result of gap-crossing behavior with up to nine robots. We show with hardware experiments that the robots are able to couple with each other to cross gaps and decouple to perform individual tasks. We anticipate the PuzzleBots will be useful in unstructured environments as individuals and coupled systems in real-world applications.

Current techniques in robot design and fabrication are time consuming and costly. Robot designs are needed that facilitate low-cost fabrication techniques and reduce the design to production timeline. Here we present an axial-rotational coupled metastructure that can serve as the functional core of a low-cost 3D printed walking robot. Using an origami-inspired assembly technique, the axial-rotational coupled metastructure robot can be 3D printed flat and then folded into a final configuration. This print-then-fold approach allows for the facile integration of critical subcomponents during the printing process. The axial-rotational metastructures eliminate the need for joints and linkages by enabling locomotion through a single compliant structure. Finite element models of the axialrotational metastructures were developed and validated against experimental deformation of 3D printed units under tensile loading. As a proof-of-concept, an ultra low-cost 3D-printed metabot was designed and fabricated using the proposed axial-rotational coupled metastructure and its walking performance was characterized. A top speed of 4.30 mm/s was achieved with an alternating stepping gait at a frequency of 0.8 Hz.

Controlled navigation of swimming micro robots inside fluid filled channels is necessary for applications in living tissues and vessels. Hydrodynamic behavior inside channels and interaction with channel walls need to be understood well for successful design and control of these surgical-tools-to-be. In this study, two different mechanisms are used for forward and lateral motion: rotation of helices in the direction of the helical axis leads to forward motion in the viscous fluid, and rolling due to wall traction results with the lateral motion near the wall. Experiments are conducted using a magnetic helical swimmer having 1.5 mm in length and 0.5 mm in diameter placed inside two different glycerol-filled channels with rectangular cross sections. The strength, direction and rotational frequency of the externally applied rotating magnetic field are used as inputs to control the position and direction of the micro swimmer in Y- and T-shaped channels.

Swimming micro robots are becoming feasible in biomedical applications such as targeted drug delivery, opening clogged arteries and diagnosis owing to recent developments in micro and nano manufacturing technologies. It has been demonstrated at various scales that micro helices with magnetic coating or attached to a magnet can move in fluids with the application of external rotating magnetic fields. The motion of micro swimmers interacting with flow inside channels needs to be well understood especially for medical applications where the motion of micro robots inside arteries and conduits in the body become pertinent. In this work, swimming of helical micro robots with magnetic heads inside tubes is modeled with the resistive force theory (RFT) and validated with experiments conducted in glycerin filled mini glass channels placed in rotational magnetic fields. The time-averaged forward velocities of magnetically driven micro swimmers that are calculated by the RFT model agree very well with experimental results.