Peripheral nerves provide a promising source of motor control signals for neuroprosthetic devices. Unfortunately, the clinical utility of current peripheral nerve interfaces is limited by signal amplitude and stability. Here, we showed that the regenerative peripheral nerve interface (RPNI) serves as a biologically stable bioamplifier of efferent motor action potentials with long-term stability in upper limb amputees. Ultrasound assessments of RPNIs revealed prominent contractions during phantom finger flexion, confirming functional reinnervation of the RPNIs in two patients. The RPNIs in two additional patients produced electromyography signals with large signal-to-noise ratios. Using these RPNI signals, subjects successfully controlled a hand prosthesis in real-time up to 300 days without control algorithm recalibration. RPNIs show potential in enhancing prosthesis control for people with upper limb loss.

We describe a new robot architecture: the collaborative robot, or cobot. Cobots are intended for direct physical interaction with a human operator. The cobot can create smooth, strong virtual surfaces and other haptic effects within a shared human/cobot workspace. The kinematic properties of cobots differ markedly from those of robots. Most significantly, cobots have only one mechanical degree of freedom, regardless of their taskspace dimensionality. The instantaneous direction of motion associated with this single degree of freedom is actively servo-controlled, or steered, within the higher dimensional taskspace. This paper explains the kinematics of cobots and the continuously variable transmissions (CVTs) that are essential to them. Powered cobots are introduced, made possible by a parallel interconnection of the CVTs. We discuss the relation of cobots to conventionally actuated robots and to nonholonomic robots. Several cobots in design, prototype, or industrial testbed settings illustrate the concepts discussed.

Hybrid Vehicle fuel economy performance is highly sensitive to the energy management strategy used to regulate power flow among the various energy sources and sinks. Optimal non-causal solutions are easy to determine if the drive cycle is known a priori. It is very challenging to design causal controllers that yield good fuel economy for a range of possible driver behavior. Additional challenges come in the form of constraints on powertrain activity, such as shifting and starting the engine, which are commonly called "drivability" metrics and can adversely affect fuel economy. In this paper, drivability restrictions are included in a Shortest Path Stochastic Dynamic Programming (SP-SDP) formulation of the real-time energy management problem for a prototype vehicle, where the drive cycle is modeled as a stationary, finite-state Markov chain. When the SP-SDP controllers are evaluated with a high-fidelity vehicle simulator over standard government drive cycles, and compared to a baseline industrial controller, they are shown to improve fuel economy more than 11% for equivalent levels of drivability. In addition, the explicit tradeoff between fuel economy and drivability is quantified for the SP-SDP controllers.

This paper describes a paradigm for human/automation control sharing in which the automation acts through a motor coupled to a machine's manual control interface. The manual interface becomes a haptic display, continually informing the human about automation actions. While monitoring by feel, users may choose either to conform to the automation or override it and express their own control intentions. This paper's objective is to demonstrate that adding automation through haptic display can be used not only to improve performance on a primary task but also to reduce perceptual demands or free attention for a secondary task. Results are presented from three experiments in which 11 participants completed a lane-following task using a motorized steering wheel on a fixed-base driving simulator. The automation behaved like a copilot, assisting with lane following by applying torques to the steering wheel. Results indicate that haptic assist improves lane following by least 30%, p < .0001, while reducing visual demand by 29%, p < .0001, or improving reaction time in a secondary tone localization task by 18 ms, p = .0009. Potential applications of this research include the design of automation interfaces based on haptics that support human/automation control sharing better than traditional push-button automation interfaces.

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Controlling soft robots with precision is a challenge due in large part to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit linear dynamical models of soft robots and to control them using established model-based linear control methods. This method is data-driven, yet unlike other data-driven models such as neural networks, it yields an explicit control-oriented linear model rather than just a "black-box" input-output mapping. This work describes this Koopman-based system identification method and its application to model predictive controller design. A model and MPC controller of a pneumatic soft robot arm is constructed via the method, and its performance is evaluated over several trajectory following tasks in the real-world. On all of the tasks, the Koopman-based MPC controller outperforms a benchmark MPC controller based on a linear state-space model of the same system.

Functional strength training is becoming increasingly popular when rehabilitating individuals with neurological injury such as stroke or cerebral palsy. Typically, resistance during walking is provided using cable robots or weights that are secured to the distal shank of the subject. However, there exists no device that is wearable and capable of providing resistance across the joint, allowing over ground gait training. In this study, we created a lightweight and wearable device using eddy current braking to provide resistance to the knee. We then validated the device by having subjects wear it during a walking task through varying resistance levels. Electromyography and kinematics were collected to assess the biomechanical effects of the device on the wearer. We found that eddy current braking provided resistance levels suitable for functional strength training of leg muscles in a package that is both lightweight and wearable. Applying resistive forces at the knee joint during gait resulted in significant increases in muscle activation of many of the muscles tested. A brief period of training also resulted in significant aftereffects once the resistance was removed. These results support the feasibility of the device for functional strength training during gait. Future research is warranted to test the clinical potential of the device in an injured population.

Each year, approximately 185,000 Americans suffer the devastating loss of a limb. The effects of upper limb amputations are profound because a person's hands are tools for everyday functioning, expressive communication, and other uniquely human attributes. Despite the advancements in prosthetic technology, current upper limb prostheses are still limited in terms of complex motor control and sensory feedback. Sensory feedback is critical to restoring full functionality to amputated patients because it would relieve the cognitive burden of relying solely on visual input to monitor motor commands and provide tremendous psychological benefits. This article reviews the latest innovations in sensory feedback and argues in favor of peripheral nerve interfaces. First, the authors examine the structure of the peripheral nerve and its importance in the development of a sensory interface. Second, the authors discuss advancements in targeted muscle reinnervation and direct neural stimulation by means of intraneural electrodes. The authors then explore the future of prosthetic sensory feedback using innovative technologies for neural signaling, specifically, the sensory regenerative peripheral nerve interface and optogenetics. These breakthroughs pave the way for the development of a prosthetic limb with the ability to feel.