Lael U. Odhner

A compliant, underactuated hand for robust manipulation

This paper introduces the iRobot-Harvard-Yale (iHY) Hand, an underactuated hand driven by five actuators that is capable of performing a wide range of grasping and in-hand repositioning tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. The primary focus of this paper will be on the novel simplified design of the iHY Hand, which was developed by choosing a set of target tasks around which the hand was optimized. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power grasps and low-stiffness fingertip grasps using only the compliant mechanics of the fingers. Experimental results demonstrate successful grasping of a wide range of target objects, the stability of fingertip grasping, and the ability to adjust the force exerted on grasped objects using high-impedance actuators and underactuated fingers.

A modular, open-source 3D printed underactuated hand

Commercially available robotic hands are often expensive, customized for specific platforms, and difficult to modify. In this paper, we present the design of an open-source, low-cost, single actuator underactuated hand that can be created through fast and commonly-accessible rapid-prototyping techniques and simple, off-the-shelf components. This project establishes the design of an adaptive, four-finger hand utilizing simple 3D-printed components, compliant flexure joints, and readily obtainable off-the-shelf parts. Modular and adjustable finger designs are provided, giving the user a range of options depending on the intended use of the hand. The design tradeoffs and decisions made to achieve the 3D-printable, compact and lightweight robotic gripper are discussed, as well as a preliminary discussion of the performance differences between the finger designs. The authors intend this work to be the first in a series of open-source designs to be released, and through the contributions of the open-source user community, result in a large number of design modifications and variations available to researchers.

Dexterous manipulation with underactuated elastic hands

In this paper we show that it is possible to design underactuated robot hands capable of performing dexterous manipulation tasks, despite the fact that the motion of an underactuated hand is not fully constrained by its actuators. If a robot has elastic elements at its joints, then the velocity of the actuators can be mapped onto the velocity of the grasped object using elastic averaging. This mapping can be used to compute classical measures of manipulability for an underactuated hand. We also demonstrate that holonomically constrained grasps can be analyzed to determine the manifold of stable object configurations that can be reached from some initial grasp. This is especially useful for planar manipulation operations, such as twisting a knob or precision positioning. A prototype two-fingered planar underactuated hand is introduced, having the ability to stably grasp and manipulate objects within the hand.

Broadcast Feedback of Stochastic Cellular Actuators Inspired by Biological Muscle Control

This paper presents a broadcast feedback approach to the distributed stochastic control of an actuator system consisting of many cellular units. This control architecture was inspired by skeletal muscles comprising a vast number of tiny functional units, called sarcomeres. The output of the actuator system is an aggregate e fect of numerous cellular units, each taking a bistable ON—OFF state. A central controller “broadcasts” the error between the aggregate output and a reference input. Rather than ordering the individual units to take specific states, the central controller merely broadcasts the overall error signal to all the cellular units uniformly. In turn each cellular unit makes a stochastic decision with a state transition probability, which is modulated in relation to the broadcasted error. Stochastic properties of both open-loop and closed-loop control systems are analyzed. Stability conditions of the broadcast feedback system are obtained by using a stochastic Lyapunov function. The proposed method is simulated for an artificial cellular actuator, consisting of many segments of smart actuator material. Theoretical results are verified through simulation. It is demonstrated that, even in the absence of deterministic coordination, the ensemble of the cellular units can track a given trajectory stably and robustly.

Open-Loop Precision Grasping With Underactuated Hands Inspired by a Human Manipulation Strategy

In this paper, we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of small objects. Taking inspiration from the human grasping strategy for picking up objects from a flat surface, we introduce the flip-and-pinch task, in which the hand picks up a thin object by flipping it into a stable configuration between two fingers. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can interact with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. Even when utilizing only open-loop kinematic playback, this approach is shown to be robust to variation in object size and hand position. Variation of up to 20° in orientation and 10 mm in hand height still result in experimental success rates of 80% or higher. These results suggest that the advantages of underactuated, adaptive robot hands can be carried over from basic grasping tasks to more dexterous tasks.

Precision grasping and manipulation of small objects from flat surfaces using underactuated fingers

In this paper we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of relatively small objects. Taking a cue from human manipulation, we introduce the flip-and-pinch task, in which the hand picks up thin objects from a table surface by flipping it into a stable configuration. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can be configured with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. This approach is shown to be robust for a variety of object sizes, even when utilizing identical open-loop kinematic playback. Experimental results suggest that the advantages of underactuated, adaptive robot hands can be carried over to dexterous, precision tasks as well.

The Smooth Curvature Model: An Efficient Representation of Euler–Bernoulli Flexures as Robot Joints

This paper presents a new method to produce computationally efficient models of robots that have planar elastic flexure joints. An accurate, low-dimensional model of large deformation bending is important to precisely describe the configuration of a flexure-jointed manipulator. The new model is based on the assumption that the curvature of a beam in bending is smooth and, thus, can be approximated by low-order polynomials. This produces a description of flexure motion that can be used as a joint model when expressed as a homogeneous transformation between rigid links--essentially a “drop in” replacement for traditional joint models such as screw coordinates and Denavit-Hartenberg conventions. Derivatives of the joint kinematics such as Jacobians and Hessians are accurate and easy to compute. We will show that with only three parameters, this model faithfully reproduces the elastic deformation of a flexure hinge predicted by the continuum model, even for large angles, without requiring numerical integration or many finite elements. The model can also be used to accurately compute the compliance and compressive buckling load of the flexure, as predicted by the continuum model.

Stable, open-loop precision manipulation with underactuated hands

This paper discusses dexterous, within-hand manipulation with differential-type underactuated hands. We discuss the fact that not only can this class of hands, which to date have been considered almost exclusively for adaptive grasping, be utilized for precision manipulation, but also that the reduction of the number of actuators and constraints can make within-hand manipulation easier to implement and control. Next, we introduce an analytical framework for evaluating the dexterous workspace of objects held within the fingertips in a precision grasp. A set of design principles for underactuated fingers are developed that enable fingertip grasping and manipulation. Finally, we apply this framework to analyze the workspace of stable object configurations for an object held within a pinch grasp of a two-fingered underactuated planar hand, demonstrating a large and useful workspace despite only one actuator per finger. The in-hand manipulation workspace for the iRobot–Harvard–Yale Hand is experimentally measured and presented.

Stochastic Recruitment Control of Large Ensemble Systems With Limited Feedback

A new approach to controlling the ensemble behavior of many identical agents is presented in this paper, inspired by motor recruitment in skeletal muscles. A group of finite state agents responds randomly to broadcast commands, each producing a state-dependent output that is measured in aggregate. Despite the lack of feedback signal and initial state information, this control architecture allows a single central controller to direct the aggregate output of the ensemble toward a desired value. First, the system is modeled as an ensemble of statistically independent, identically distributed, binary-state Markov processes with state transition probabilities designated by a central controller. Second, steady-state behavior, convergence rate, and variance of the aggregate output, i.e., the total number of recruited agents, are analyzed, and design trade-offs in terms of accuracy, convergence speed, and the number of spurious transitions are made. Third, a limited feedback signal, only detecting if the output has reached a goal, is added to the system, and the recruitment controller is designed as a stochastic shortest path problem. Optimal convergence rate and associated transition probabilities are obtained. Finally, the theoretical results are verified and demonstrated with both numerical simulation and control of an artificial muscle actuator made up of 60 binary shape memory alloy motor units.

Printing Three-Dimensional Electrical Traces in Additive Manufactured Parts for Injection of Low Melting Temperature Metals

While techniques exist for the rapid prototyping of mechanical and electrical components separately, this paper describes a method where commercial additive manufacturing (AM) techniques can be used to concurrently construct the mechanical structure and electronic circuits in a robotic or mechatronic system. The technique involves printing hollow channels within 3D printed parts that are then filled with a low melting point liquid metal alloy that solidifies to form electrical traces. This method is compatible with most conventional fused deposition modeling and stereolithography (SLA) machines and requires no modification to an existing printer, though the technique could easily be incorporated into multimaterial machines. Three primary considerations are explored using a commercial fused deposition manufacturing (FDM) process as a testbed: material and manufacturing process parameters, simplified injection fluid mechanics, and automatic part generation using standard printed circuit board (PCB) software tools. Example parts demonstrate the ability to embed circuits into a 3D printed structure and populate the surface with discrete electronic components. [DOI: 10.1115/1.4029435]