In this work, we present a methodology for enabling a robot to identify an object and grasp configuration of interest and assist the human teleoperating the robot, to grasp the object. The identification is carried out in real-time by detecting the motion intention of the human as they are teleoperating the remote robotic arm towards the object and the grasp configuration. Simultaneously, depending on the detected object and grasp configuration, the human user is assisted to translate and orient the remote arm gripper in order to preshape and grasp the object. The complete process occurs with the human teleoperating the arm, and without them having to interact with another interface. Motion intention recognition is carried out by using Hidden Markov Models (HMMs), trained offline by preshape trials performed by a skilled teleoperator. The environment is unstructured and comprises of a number of objects, each with multiple grasp configurations. Experimental tests on healthy human subjects have validated our intention recognition based assistance method. They show that the method allows objects to be grasped and placed 48% faster, and with much ease compared to unassisted teleoperation. Moreover, we have proved that the model for intention recognition, trained by a skilled teleoperator, can be used by novice users to efficiently execute a grasping task in teleoperation.

This paper presents a Gait Enhancing Mobile Shoe (GEMS) that mimics the desirable kinematics of a split-belt treadmill except that it does so over ground. Split-belt treadmills, with two separate treads running at different speeds, have been found useful in the rehabilitation of persons with asymmetric walking patterns. Although in preliminary testing, beneficial after-effects have been recorded, various drawbacks include the stationary nature of the split-belt treadmill and the inability to keep a person on the split-belt treadmill for an extended period of time. For this reason, the after-effects for long-term gait training are still unknown. The mobile ability of the GEMS outlined in this paper enables it to be worn in different environments such as in one's own house and also enables it to be worn for a longer period of time since the GEMS is completely passive. Healthy subject testing has demonstrated that wearing this shoe for twenty minutes can alter the wearer's gait and will generate after-effects in a similar manner as a split-belt treadmill does.

Insertion velocity, tip asymmetry, and shaft diameter may influence steerable needle insertion paths in soft tissue. In this paper we examine the effects of these variables on needle paths in ex vivo goat liver, and demonstrate practical applications of robotic needle steering for ablation, biopsy, and brachytherapy. All experiments were performed using a new portable needle steering robot that steers asymmetric-tip needles under fluoroscopic imaging. For bevel-tip needles, we found that larger diameter needles resulted in less curvature, i.e. less steerability, confirming previous experiments in artificial tissue. The needles steered with radii of curvature ranging from 3:4 cm (for the most steerable pre-bent needle) to 2:97m (for the least steerable bevel needle). Pre-bend angle significantly affected needle curvature, but bevel angle did not. We hypothesize that biological tissue characteristics such as inhomogeneity and viscoelasticity significantly increase path variability. These results underscore the need for closed-loop image guidance for needle steering in biological tissues with complex internal structure.

Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.

In this paper, we demonstrate the use of range information from a laser sensor mounted on the end-effector of a remote robot manipulator to assist persons with limited upper body strength carry out Activities of Daily Living in unstructured environments. Laser range data is used to determine goals, identify targets, obstacles, and via points to enable autonomous execution of trajectories. The human operator is primarily involved in higher level decision making; the human user performs minimal teleoperation to identify critical points in the workspace with the laser pointer. Tests on ten healthy human subjects in executing a pick-and-place task showed that laser-based assistance not only increased the speed of task execution by an average of 26.9%while decreasing the physical effort by an average of 85.4%, but also made the task cognitively easier for the user to execute.

Many daily tasks require that a person use both hands simultaneously, such as moving a large book or opening the lid on a jar. Such bimanual tasks are difficult for people who have a stroke, but the tight neural coupling across the body has been hypothesized to allow individuals to self-rehabilitate by physically coupling their hands. To examine potential methods for robot-assisted bimanual rehabilitation, we performed a haptic tracking task where individuals experience a one degree of freedom trajectory on one hand and attempt to recreate it with their other hand. Despite the biomechanical and neurological symmetries present across the human body, subjects performed this task worse when working in the joint space (i.e., mirrored motion) than they did in the visually centered space. We also examined multiple input paths and show alternative rhythmic motions that may aid in rehabilitation.

The asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. In this study we present a mechanics-based model that calculates the deflection of the needle embedded in an elastic medium. Microscopic observations for several needle-gel interactions were used to characterize the interactions at the bevel tip and along the needle shaft. The model design was guided by microscopic observations of several needle-gel interactions. The energy-based model formulation incorporates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot-driven needle interacting with different kinds of gels. These results contribute to a mechanics-based model of robotic needle steering, extending previous work on kinematic models.

A flexible needle can be accurately steered by robotically controlling the orientation of the bevel tip as the needle is inserted into tissue. Here, we demonstrate the significant effect of friction between the long, flexible needle shaft and the tissue, which can cause a significant discrepancy between the orientation of the needle tip and the orientation of the base where the needle is controlled. Our experiments show that several common phantom tissues used in needle steering experiments impart substantial frictional forces to the needle shaft, resulting in a lag of over 45° for a 10 cm insertion depth in some phantoms; clinical studies have reported torques large enough to could cause similar errors during needle insertions. Such angle discrepancies will result in poor performance or failure of path planners and image-guided controllers, since the needles used in percutaneous procedures are too small for state-of-the-art imaging to accurately measure the tip angle. To compensate for the angle discrepancy, we develop a model for the rotational dynamics of a needle being continuously inserted into tissue and show how a PD controller is sufficient to compensate for the rotational dynamics.

Machines might physically interact with humans more smoothly if we better understood the subtlety of human-human physical interaction. We recently reported that two people working cooperatively on a physical task will quickly negotiate an emergent strategy: typically subjects formed a temporal specialization such that one member commands the early parts of motion and the other the late parts. In our study, we replaced one of the humans with a robot programmed to perform one of the typical human specialized roles. We expected the remaining human to adopt the complementary specialized role. Subjects did believe that they were interacting with another human but did not adopt a specialized behavior as subjects would when physically working with another human; our negative result suggests a very subtle negotiation takes place in human-human physical interaction.

Haptic interaction between people and machines might benefit from an understanding of haptic communication between one person and another. We recently reported results showing that two people performing a physically shared dyadic task can outperform either person alone, even when the perception of each participant is that the other is a hindrance. Evidently a dyad quickly negotiates a more efficient motion strategy than is available to individuals. This negotiation must take place through a haptic channel of communication, and it is apparently at a level below the awareness of the participants. Here we report results on the motion strategy that emerged. By recording forces and motions we show that the dyads "specialized" temporally such that one member took on early parts of the motion and the other late parts. Tests in which one participant's contribution was surreptitiously replaced by a motor did not elicit a similar cooperative response from the remaining human participant, showing that the language of haptic communication between people must be rather subtle

Often two people must work together physically on a common task, such as lifting and positioning; a long board, or, in our model experimental system, turning a two-handled crank. Such tasks involve communication between the people, mediated by the task kinematics and dynamics: each person feels forces and motions produced by the other and derive some meaning from them. Tasks may include a degree of competition: the two people may not have exactly the same goal in mind, and must negotiate a compromise. Understanding human-human communication is important in designing robots for interaction with humans, and for robots that provide powered assistance for human-human tasks (such as physical therapy). In this paper we describe early experiments in human-human physical interaction, with a 1 dof robot included in order to give experimental access to the exchange of forces and motions between the people. We report on Fitts' law-like tasks, in which the two people cooperate to move a cursor to a common target, or to targets that do not completely overlap. Our results suggest that human-human physical communication may be a rich area of study.