Ravi Balasubramanian - US grants

Implantable passive mechanisms have never been developed for orthopedic surgery. This project seeks to create a paradigm shift in the domain of reconstructive orthopedic surgery through the exploration of the commercial market for a new class of implantable biomedical devices. The implants are passive mechanisms that take the form of tendon networks, levers, pulleys, and linkages. They will be surgically placed between muscles and tendons within the body with the goal of modifying the functional attachment of muscles to tendons and bone. Specifically, the implants will provide the ability to scale and distribute forces better between muscles and tendons when compared with the current practice of using sutures to make attachments. These implants will enable customizable and superior musculoskeletal function for the patient, thus improving quality of life in the long term. Since such implants have never been developed before, this project promotes the progress of science. If these implants make it to market, the work could lead to the creation of a new medical-device industry, thus advancing national health, prosperity and welfare.

The work currently focuses on two classes of implantable passive mechanisms as exemplar implants. The first class consists of 'differential' mechanisms, such as moving pulleys and levers, which distribute forces and movements from one muscle across multiple tendons. The second class of implants consists of 'force-scaling' mechanisms that increase the force transferred from one muscle to a tendon. Preliminary studies show that these implants enable customizable and superior musculoskeletal function for the patient, thus improving quality of life in the long term. During the I-Corps program, each member of the team will pursue activities every week that explore the commercialization potential of the proposed technology. Specifically, the team will bring together ideas regarding the need for such implant technology in the surgical domain by consulting with people from different backgrounds, such as companies that develop orthopedic implants, regulatory bodies, and medical care centers.

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Balasubramanian

This project designs miniature implantable mechanisms for attaching muscles to tendons and bone in orthopedic surgery. The implants take the form of passive mechanisms, such as soft tendon networks, pulleys, and linkages. Instead of directly attaching muscles to tendons and bone using sutures, a surgeon can use the passive implants to re-route and re-arrange how a patient's tendons create movement at the joint. By doing so, the implants enable increased force or range of motion at a joint using the patient's own muscles and without using any external power or electronics. Overall, the implants enable new surgeries that provide the patient improved and customized manipulation and locomotion function when compared with the current suture-based surgical paradigm. In terms of impact, this project is expected improve quality of life for the disabled, seniors, and veterans, lead to new research in allied areas such as biomaterials and bioethics, and lead to new medical devices.

The research develops a framework for the design of such implantable mechanisms after quantifying the movement deficits that arise in the current suture-based surgical paradigm. In particular, this project develops implants for two surgical applications: an implant that differentially distributes forces and movement from one muscle to multiple tendons for use in a hand surgery to restore grasping capability following median-ulnar nerve palsy; and an implant that scales up forces for use in a foot surgery to restore the collapsed foot arch. The implants will be validated through biomechanical simulations, human and animal cadaver experiments, and live-animal experiments. This project integrates this research with extensive educational activities. Specifically, this project creates new curricula using hands-on open source hardware and software to train the next generation of leaders at the middle-school, high-school, undergraduate, graduate, and teacher levels in understanding the mechanics of human body movement and using robotics to improve human body movement. The project also includes outreach programs to increase participation of women and under-represented minorities in the STEM fields within a culture of inclusivity and equality.

The robotics community has a rich history of research and development in teaching and training robots to pick up and manipulate objects. However, it has proven to be very difficult to transition this research from structured laboratory settings to real world settings, such as homes, small-scale industrial settings, and search and rescue domains. The development of large-scale, standardized testing and benchmarking of robotic manipulation approaches is necessary to move robotic manipulation from the research lab to the real world. Placing the burden of conducting these tests on every individual robotics researcher is inefficient, at best. Cleaning the captured data to make it available to other researchers requires additional work for the producers of the data - and even more work on potential users. All of this impedes progress for the community as a whole, and makes it difficult to bring to bear recent developments in deep learning. This project addresses this problem by setting the groundwork for a dedicated physical robotic grasping and manipulation testbed infrastructure that can be remotely accessed and operated by anyone doing research in this area.

This testbed will provide several critical components that the robotics grasping and manipulation community needs: (i) "Test suites" that enable repeated testing and controlled manipulation of several variables that have confounded robotic grasping and manipulation research. Variables include object and gripper material properties, compliance, force/torque of physical interaction, mass of manipulator elements and the objects, surface texture, low-level control algorithms, and higher-level planning techniques. (ii) Extensive instrumentation to capture (nearly) all aspects of the physical interaction, such as the forces, kinematics of movement, and three-dimensional geometry. (iii) A modular and customizable human-robot interface for enabling robotic physical interaction. Users will be able to directly control the robot using low-level interfaces, such as the knobs that control the movements of individual joints, or use higher-level interfaces that encapsulate robot-object interactions, such as close the fingers until they contact the object. (iv) Data collection, where the data will be made publicly available in a standardized form. The focus of this planning activity is to develop the necessary technological elements to prove the feasibility of such a test facility (automated object return in a fully instrumented space, a fully instrumented "door" to evaluate opening and closing doors) and to evaluate the community's needs in this area.

The robotics community has a rich history of research and development in grasping and manipulation. However, it has proven to be very difficult to make grasping work in the real world (e.g., homes and small-scale industry). Part of the problem is the limited amount of testing that can be done in a lab. Testing requires specialized hardware and a very large number of trials, which is difficult for a single researcher to do on their own. The goal of this project is to set up dedicated test centers for grasping and manipulation that can be used by anyone with an internet connection. The test centers will provide standardized benchmarks, software and tutorials to teach people the basics of grasping, and hardware to perform the actual testing. This infrastructure will make it easier for people to share and compare results, and make it easier for people to contribute without needing to buy (and maintain) a large amount of specialized hardware.

There are four parts to this proposal; 1) Developing specialized hardware for testing and deploying in two facilities (one at Oregon State University, one at the University of Massachusetts Lowell) that have dedicated robotic arms and manipulators; 2) Software infrastructure to enable remote access for specifying tasks, running those tasks, then visualizing the results; 3) Implementing new and existing benchmark protocols (such as the ones developed at the National Institute of Standards and Technology (NIST)); and 4) Developing a community of users, including academic, industrial, and governmental institutions. The testbed approach should allow new and existing researchers a low cost method to participating in the grasping and manipulation scientific community and to provide standards from which the field can grow.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

People are incredibly good at picking up and manipulating objects. Robots, however, still struggle with reliably picking up objects. The goal of this project is to improve robotic grasping by observing how humans respond to challenging grasping situations. The project's central idea is that humans strive for grasp success rather than trying to find the best possible grasp. Humans use a grasp that is likely to work, even if they have misjudged where the object is, what shape it is, how heavy it is, etc. Finding what these strategies are (in a form a robot can use) will improve robotic grasping in the "real world".

The technical challenge addressed in this project is how to go from human demonstrations all the way to robot hand control strategies. First, human user study participants will demonstrate grasping with actual robotic hands by "puppeteering" the hands through a structured set of grasping and manipulation tasks. This demonstration will then be reconstructed in a physics simulator. This provides a rich set of data in an environment - grounded in physical reality - that algorithms can learn in. Finally, the data is put in a novel form that is ideally suited to capturing what happens as the hand comes into contact with the object in a form suitable for machine learning. The result is a learned set of near-contact robotic controllers that can be incorporated into existing grasp planning algorithms in order to improve robotic grasping.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.