Whole-Hand Robotic Manipulation with Rolling, Sliding, and Caging

Traditional manipulation planning and modeling relies on strong assumptions about contact. Specifically, it is common to assume that contacts are fixed and do not slide. This assumption ensures that objects are stably grasped during every step of the manipulation, to avoid ejection. However, this assumption limits achievable manipulation to the feasible motion of the closed-loop kinematic chains formed by the object and fingers. To improve manipulation capability, it has been shown that relaxing contact constraints and allowing sliding can enhance dexterity. But in order to safely manipulate with shifting contacts, other safeguards must be used to protect against ejection. “Caging manipulation,” in which the object is geometrically trapped by the fingers, can be employed to guarantee that an object never leaves the hand, regardless of constantly changing contact conditions. Mechanical compliance and underactuated joint coupling, or carefully chosen design parameters, can be used to passively create a caging grasp – protecting against accidental ejection – while simultaneously manipulating with all parts of the hand. And with passive ejection avoidance, hand control schemes can be made very simple, while still accomplishing manipulation. In place of complex control, better design can be used to improve manipulation capability—by making smart choices about parameters such as phalanx length, joint stiffness, joint coupling schemes, finger frictional properties, and actuator mode of operation. I will present an approach for modeling fully actuated and underactuated whole-hand-manipulation with shifting contacts, show results demonstrating the relationship between design parameters and manipulation metrics, and show how this can produce highly dexterous manipulators

Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects

We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints and settings, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional C-spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional C-spaces but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We present 8 simulation experiments demonstrating the applicability of our approach to various complex quasi-static manipulation scenarios. Quantitative results indicate the effectiveness of our approach in providing upper-bound estimates for escape energy in quasi-static manipulation scenarios. Two real-world experiments also show that the computed normalized escape energy estimates appear to correlate strongly with the probability of escape of an object under randomized pose perturbation.Comment: Paper submitted to ICRA 202

Flexible Object Manipulation

Flexible objects are a challenge to manipulate. Their motions are hard to predict, and the high number of degrees of freedom makes sensing, control, and planning difficult. Additionally, they have more complex friction and contact issues than rigid bodies, and they may stretch and compress. In this thesis, I explore two major types of flexible materials: cloth and string. For rigid bodies, one of the most basic problems in manipulation is the development of immobilizing grasps. The same problem exists for flexible objects. I have shown that a simple polygonal piece of cloth can be fully immobilized by grasping all convex vertices and no more than one third of the concave vertices. I also explored simple manipulation methods that make use of gravity to reduce the number of fingers necessary for grasping. I have built a system for folding a T-shirt using a 4 DOF arm and a fixed-length iron bar which simulates two fingers. The main goal with string manipulation has been to tie knots without the use of any sensing. I have developed single-piece fixtures capable of tying knots in fishing line, solder, and wire, along with a more complex track-based system for autonomously tying a knot in steel wire. I have also developed a series of different fixtures that use compressed air to tie knots in string. Additionally, I have designed four-piece fixtures, which demonstrate a way to fully enclose a knot during the insertion process, while guaranteeing that extraction will always succeed

Grasping and Assembling with Modular Robots

A wide variety of problems, from manufacturing to disaster response and space exploration, can benefit from robotic systems that can firmly grasp objects or assemble various structures, particularly in difficult, dangerous environments. In this thesis, we study the two problems, robotic grasping and assembly, with a modular robotic approach that can facilitate the problems with versatility and robustness. First, this thesis develops a theoretical framework for grasping objects with customized effectors that have curved contact surfaces, with applications to modular robots. We present a collection of grasps and cages that can effectively restrain the mobility of a wide range of objects including polyhedra. Each of the grasps or cages is formed by at most three effectors. A stable grasp is obtained by simple motion planning and control. Based on the theory, we create a robotic system comprised of a modular manipulator equipped with customized end-effectors and a software suite for planning and control of the manipulator. Second, this thesis presents efficient assembly planning algorithms for constructing planar target structures collectively with a collection of homogeneous mobile modular robots. The algorithms are provably correct and address arbitrary target structures that may include internal holes. The resultant assembly plan supports parallel assembly and guarantees easy accessibility in the sense that a robot does not have to pass through a narrow gap while approaching its target position. Finally, we extend the algorithms to address various symmetric patterns formed by a collection of congruent rectangles on the plane. The basic ideas in this thesis have broad applications to manufacturing (restraint), humanitarian missions (forming airfields on the high seas), and service robotics (grasping and manipulation)

Quantifying grasp quality using an inverse reinforcement learning algorithm

This thesis considers the problem of using a learning algorithm to recognize when a mechanical gripper and sensor combination has achieved a robust grasp. Robotic hands are continuously evolving with finer motor control and higher degrees of freedom which can complicate the ability of an operator to determine if a gripper has achieved a successful grasp. Robots working in hazardous environments especially need confirmation of a successful grasp as the cost of failure is often higher than in traditional factory environments. The object set found in a nuclear environment is the focus of this effort. Objects in this environment are typically expensive (or one-of-a-kind), rigid, radioactive (or toxic), dense, and susceptible to dents, scratches, and oxidation. To validate the robustness of a grasp option, an online inverse reinforcement learning approach is evaluated as a method to quantify grasp quality. This approach is applied to an industrial-grade under-actuated robotic hand equipped with 36 pressure sensors. An expert trains the inverse reinforcement learning algorithm to generate a reward function which scores each grasp so - when combined with fuzzy logic - provides a general success or fail along with a confidence level. Utilizing the trained inverse reinforcement learning algorithm in a glovebox environment reduces the number of potential failing and untrustworthy grasps by scoring executed grasps and rejecting grasps that are similar to prior failed grasps while allowing further execution of movement when a grasp has been scored highly. The trained algorithm incorrectly classified grasps of insufficient quality less than 5% of the time in experimental hardware tests, showing that the algorithm can be applied to the glovebox environment to improve grasp safety. Thus the combination of grasp selection and pressure sensor validation provides a more efficient, robust, and redundant method to assure items can be safely handled during remote automation processes.Mechanical Engineerin

Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands

The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis