Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark

This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated suction grasp datasets. However, the generation of suction grasp datasets in cluttered environments remains underexplored, leaving uncertainties about the relationship between the object of interest and its surroundings. To address this, we propose a benchmark synthetic dataset, Sim-Suction-Dataset, comprising 500 cluttered environments with 3.2 million annotated suction grasp poses. The efficient Sim-Suction-Dataset generation process provides novel insights by combining analytical models with dynamic physical simulations to create fast and accurate suction grasp pose annotations. We introduce Sim-Suction-Pointnet to generate robust 6D suction grasp poses by learning point-wise affordances from the Sim-Suction-Dataset, leveraging the synergy of zero-shot text-to-segmentation. Real-world experiments for picking up all objects demonstrate that Sim-Suction-Pointnet achieves success rates of 96.76%, 94.23%, and 92.39% on cluttered level 1 objects (prismatic shape), cluttered level 2 objects (more complex geometry), and cluttered mixed objects, respectively. The Sim-Suction policies outperform state-of-the-art benchmarks tested by approximately 21% in cluttered mixed scenes.Comment: IEEE Transactions on Robotic

3D-Printed Microswimmers with Nanostructures for Color Tracking

Two-Photon Polymerization (TPP) is a fabrication technique based on the localized linking of photosensitive materials resulting from femtosecond – a quadrillionth of a second – exposure to a laser. Such materials are based on building blocks named monomers that combine under certain stimuli (i.e. light) to form chains or complex networks. Utilization of TPP as a method for micro-3D printing has expanded the field of microrobotics, which presents medical solutions for minimizing procedure invasiveness as well as increasing treatment and diagnosis accuracy. One of the challenges in achieving desired accuracies is designing trackable features onto a microrobot. With the capabilities of TPP, we propose the construction of patterns on microrobot surfaces, mimicking color-expressing nanostructures present on beetles and butterflies. In this study, a tracking point is defined by these patterns on top of a surface on a helical microswimmer. A side-by-side comparison of various patterns determined which responds favorably to visible light. Microswimmers are decorated with the structures that elicit bright and stable reflections, and the whole design is printed and magnetized. The helix moves using an external rotating magnetic field and the color expressing features of the microswimmer are visible. Many microrobotic tracking systems are vision-based, thus, this patterning technique has the potential to mark multiple microrobots for differentiation, or even specific features on a singular robot to track displacement-type functionalities for improvement of microrobot control

A Collaborative Visual Localization Scheme for a Low-Cost Heterogeneous Robotic Team with Non-Overlapping Perspectives

This paper presents and evaluates a relative localization scheme for a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Results show that the sources of error, image quantization, asynchronous sensors, and a non-stationary bias, were sufficiently modeled to estimate the pose of the aerial robot. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team, consisting of a UAV and a UGV tasked with collaboratively localizing themselves while avoiding obstacles in an unknown environment. The team is able to identify a goal location and obstacles in the environment and plan a path for the UGV to the goal location. The results demonstrate localization accuracies of 2cm to 4cm, on average, while the robots operate at a distance from each-other between 1m and 4m

Development of a Shape Memory Polymer Soft Microgripper

The ability to control microrobots by means of magnetic fields has become of increasing interest to researchers. These robots’ ability to reach places tethered microrobots otherwise could not leads to many possible applications in the body, such as delivering drugs to targeted locations and performing biopsies. This study shows the use of shape memory polymer (SMP) to wirelessly actuate a microgripper to be used by a controllable microrobot to achieve these functions. Many smart materials were analyzed in order to find the material that most effectively would accomplish wirelessly gripping, manipulating, and releasing a microobject. Multiple microgripper designs were designed, analyzed, and constructed at a macroscale from acrylic, simulating a microscale counterpart. Simulated and experimental data were compared to determine the design that would require the least amount of inputted force and displacement from the SMP. This study shows a proposal for scaling this final design to the microscale involving experimentation with different forms of SMP in order to make the gripper actuatable in biologically relevant conditions. This technology could provide an inexpensive and effective solution for manipulating cells and other microobjects in vitro and in vivo

Improved Microrobotic Control through Image Processing and Automated Hardware Interfacing

Untethered submilliliter-sized robots (microrobots) are showing potential use in different industrial, manufacturing and medical applications. A particular type of these microrobots, magnetic robots, have shown improved performance in power and control capabilities compared to the other thermal and electrostatic based robots. However, the magnetic robot designs have not been assessed in a robust manner to understand the degree of control in different environments and their application feasibility. This research project seeks to develop a custom control software interface to provide a holistic tool for researchers to evaluate the microrobotic performance through advance control features. The software deliverable involved two main aspects: 1) Real-time microrobot detection and tracking through image processing, achieved through testing with different combinations of built-in tracking algorithms in OpenCV package, and 2) hardware interfacing with a microcontroller based coil control system through serial port communication for direct control of the magnetic coils. The robotic motion control was studied using error mode correction strategies to provide a robust, accurate and time efficient image stream based robotic controls. The user interface developed conducts change in brightness and rotation invariant tracking with an efficient speed of 12 frames per second and performs real-time calculation of robot’s position and orientation. It provides robust automatic control of directing microrobotic motion along the specific path waypoints entered on the images, through recursive serial bus communication. The project showcases the advanced importance and the powerful tool of image processing and microcontroller based communication in conducting the performance analysis of promising microrobotic designs

Linking changes in pain severity to changes in other outcomes in patients with posttraumatic peripheral neuropathic pain treated with pregabalin [POSTER]

No abstract available

Micro-Manipulation Using Learned Model

Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that will yield a desired part trajectory. Conclusions and recommendations are based on evaluations of a collection of machine learning models and the effects of training data size and hyperparameter tuning on a collection of error metrics