This paper presents an overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning, and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge.

We present a novel robotic grasp controller that allows a sensorized parallel jaw gripper to gently pick up and set down unknown objects once a grasp location has been selected. Our approach is inspired by the control scheme that humans employ for such actions, which is known to centrally depend on tactile sensation rather than vision or proprioception. Our controller processes measurements from the gripper's fingertip pressure arrays and hand-mounted accelerometer in real time to generate robotic tactile signals that are designed to mimic human SA-I, FA-I, and FA-II channels. These signals are combined into tactile event cues that drive the transitions between six discrete states in the grasp controller: Close, Load, Lift and Hold, Replace, Unload, and Open. The controller selects an appropriate initial grasping force, detects when an object is slipping from the grasp, increases the grasp force as needed, and judges when to release an object to set it down. We demonstrate the promise of our approach through implementation on the PR2 robotic platform, including grasp testing on a large number of real-world objects.

Shape control of continuum robots requires a means of sensing the the curved shape of the robot. Since continuum robots are deformable, they take on shapes that are general curves in space, which are not fully defined by actuator positions. Vision-based shape-estimation provides a promising avenue for shape-sensing. While this is often facilitated by fiducial markers, sometimes fiducials are not feasible due to either the robot's application or its size. To address this, we present a robust and efficient stereo-vision-based, shape-sensing algorithm for continuum robots that does not rely on fiducials or assume orthogonal camera placement. The algorithm employs self-organizing maps to triangulate three-dimensional backbone curves. Experiments with an object with a known shape demonstrate an average accuracy of 1.53 mm on a 239 mm arc length curve.

Sliding a probe over a textured surface generates a rich collection of vibrations that one can easily use to create a mental model of the surface. Haptic virtual environments attempt to mimic these real interactions, but common haptic rendering techniques typically fail to reproduce the sensations that are encountered during texture exploration. Past approaches have focused on building a representation of textures using a priori ideas about surface properties. Instead, this paper describes a process of synthesizing probe-surface interactions from data recorded from real interactions. We explain how to apply the mathematical principles of Linear Predictive Coding (LPC) to develop a discrete transfer function that represents the acceleration response under specific probe-surface interaction conditions. We then use this predictive transfer function to generate unique acceleration signals of arbitrary length. In order to move between transfer functions from different probe-surface interaction conditions, we develop a method for interpolating the variables involved in the texture synthesis process. Finally, we compare the results of this process with real recorded acceleration signals, and we show that the two correlate strongly in the frequency domain.

This paper outlines how a control moment gyroscope can be used to generate haptic torque feedback while minimizing the effects of a constrained gimbal workspace. We present the design of the iTorqU 2.1 and discuss how it compares to previously developed systems. We then detail the control algorithms we have developed for both transparency and torque output modes. The prescribed transparency controller is typical in design, but the torque output algorithm is novel to this type of haptic device. It makes use of a series of position-p-at-time-t commands, which we call packets. Five packet designs were considered in this research, but we have included only the most important three in this paper. While this research deals with torque feedback, it ultimately presents a method for working with devices that are limited by the need for continuous reset to a home position before subsequent outputs.

Almost all commercially available haptic interfaces share a common pitfall, a small shoebox-sized workspace; these devices typically rely on rigid-link manipulator design concepts. In this paper we outline our design for a new kinesthetic haptic system that drastically increases the usable haptic workspace. We present a proof-of-concept prototype, along with our analysis of its capabilities. Our design uses optical tracking to sense the position of the device, and air jet actuation to generate forces. By combining these two technologies, we are able to detach our device from the ground, thus sidestepping many problems that have plagued traditional haptic devices including workspace size, friction, and inertia. We show that optical tracking and air jet actuation successfully enable kinesthetic haptic interaction with virtual environments. Given an appropriately large volume high-pressure air source, and a reasonably high speed tracking system, this design paradigm has many desirable qualities when compared to traditional haptic design schemes.

This paper presents a new class of thin, dexterous continuum robots, which we call active cannulas due to their potential medical applications. An active cannula is composed of telescoping, concentric, precurved superelastic tubes that can be axially translated and rotated at the base relative to one another. Active cannulas derive bending not from tendon wires or other external mechanisms but from elastic tube interaction in the backbone itself, permitting high dexterity and small size, and dexterity improves with miniaturization. They are designed to traverse narrow and winding environments without relying on ldquoguidingrdquo environmental reaction forces. These features seem ideal for a variety of applications where a very thin robot with tentacle-like dexterity is needed. In this paper, we apply beam mechanics to obtain a kinematic model of active cannula shape and describe design tools that result from the modeling process. After deriving general equations, we apply them to a simple three-link active cannula. Experimental results illustrate the importance of including torsional effects and the ability of our model to predict energy bifurcation and active cannula shape.

Active cannulas are remotely actuated thin continuum robots with the potential to traverse narrow and winding environments without relying on "guiding" environmental reaction forces. These features seem ideal for procedures requiring passage through narrow openings to access air-filled cavities (e.g. surgery in the throat and lung). Composed of telescoping concentric pre-curved elastic tubes, an active cannula is actuated at its base by translation and axial rotation of component tubes. Using minimum energy principles and Lie Group theory, we present a framework for the kinematics of multi-link active cannulas. This framework permits testing of the hypothesis that overall cannula shape locally minimizes stored elastic energy. We evaluate in particular whether the torsional energy in the long, straight transmission between actuators and the curved sections is important. Including torsion in the kinematic model enables us to analytically predict experimentally observed bifurcation in the energy landscape. Independent calibration procedures based on bifurcation and tip and feature positions enable model parameter identification, producing results near ranges expected from tube material properties and geometry. Experimental results validate the kinematic framework and demonstrate the importance of modeling torsional effects in order to describe bifurcation and accurately predict active cannula shape.

Needles are commonly used in medical practice as a minimally invasive means to reach subsurface targets for diagnosis or therapy delivery. Recent results indicate that steerable needles may enhance targeting accuracy and allow needles to avoid obstacles along the path to the target. This work considers teleoperation of needles made of a superelastic alloy that steer through tissue using forces generated by the standard asymmetric bevel tip. The needle may be modeled as a nonholonomic system, with inputs of insertion along and spin about the needle axis. A teleoperation system consisting of a commercial master haptic device, a custom needle-steering robot slave, and visual feedback to the operator was assembled. Human subjects experiments were performed to evaluate targeting accuracy in phantom tissue for three needle control methods: teleoperation of both insertion and spin, teleoperation of insertion with open-loop-controlled spin, and open-loop control of both insertion and spin. Targeting accuracy improved with increasing degrees of freedom of human (teleoperation) control, primarily because tissue deformation and modeling limitations result in open-loop control errors. Subjects typically performed multiple spins of the needle during insertion in order to fine tune the needle path. In addition, position, rate, and a nonlinear hybrid control were compared during teleoperation of the insertion degree of freedom. The hybrid method resulted in significantly better targeting accuracy