Continuous Description of Human 3D Motion Intent through Switching Mechanism

© 2001-2011 IEEE. Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks

Soft phototactic swimmer based on self-sustained hydrogel oscillator.

Oscillations are widely found in living organisms to generate propulsion-based locomotion often driven by constant ambient conditions, such as phototactic movements. Such environment-powered and environment-directed locomotions may advance fully autonomous remotely steered robots. However, most man-made oscillations require nonconstant energy input and cannot perform environment-dictated movement. Here, we report a self-sustained soft oscillator that exhibits perpetual and untethered locomotion as a phototactic soft swimming robot, remotely fueled and steered by constant visible light. This particular out-of-equilibrium actuation arises from a self-shadowing-enabled negative feedback loop inherent in the dynamic light-material interactions, promoted by the fast and substantial volume change of the photoresponsive hydrogel. Our analytical model and governing equation unveil the oscillation mechanism and design principle with key parameters identified to tune the dynamics. On this autonomous oscillator platform, we establish a broadly applicable principle for converting a continuous input into a discontinuous output. The modular design can be customized to accommodate various forms of input energy and to generate diverse oscillatory behaviors. The hydrogel oscillator showcases agile life-like omnidirectional motion in the entire three-dimensional space with near-infinite degrees of freedom. The large force generated by the powerful and long-lasting oscillation can sufficiently overcome water damping and effectively self-propel away from a light source. Such a hydrogel oscillator-based all-soft swimming robot, named OsciBot, demonstrated high-speed and controllable phototactic locomotion. This autonomous robot is battery free, deployable, scalable, and integratable. Artificial phototaxis opens broad opportunities in maneuverable marine automated systems, miniaturized transportation, and solar sails

Straight-Leg Walking Through Underconstrained Whole-Body Control

We present an approach for achieving a natural, efficient gait on bipedal robots using straightened legs and toe-off. Our algorithm avoids complex height planning by allowing a whole-body controller to determine the straightest possible leg configuration at run-time. The controller solutions are biased towards a straight leg configuration by projecting leg joint angle objectives into the null-space of the other quadratic program motion objectives. To allow the legs to remain straight throughout the gait, toe-off was utilized to increase the kinematic reachability of the legs. The toe-off motion is achieved through underconstraining the foot position, allowing it to emerge naturally. We applied this approach of under-specifying the motion objectives to the Atlas humanoid, allowing it to walk over a variety of terrain. We present both experimental and simulation results and discuss performance limitations and potential improvements.Comment: Submitted to 2018 IEEE International Conference on Robotics and Automatio

Gesture Modeling by Hanklet-based Hidden Markov Model

In this paper we propose a novel approach for gesture modeling. We aim at decomposing a gesture into sub-trajectories that are the output of a sequence of atomic linear time invariant (LTI) systems, and we use a Hidden Markov Model to model the transitions from the LTI system to another. For this purpose, we represent the human body motion in a temporal window as a set of body joint trajectories that we assume are the output of an LTI system. We describe the set of trajectories in a temporal window by the corresponding Hankel matrix (Hanklet), which embeds the observability matrix of the LTI system that produced it. We train a set of HMMs (one for each gesture class) with a discriminative approach. To account for the sharing of body motion templates we allow the HMMs to share the same state space. We demonstrate by means of experiments on two publicly available datasets that, even with just considering the trajectories of the 3D joints, our method achieves state-of-the-art accuracy while competing well with methods that employ more complex models and feature representations

Modelling the Galaxy in the era of Gaia

The body of photometric and astrometric data on stars in the Galaxy has been growing very fast in recent years (Hipparcos/Tycho, OGLE-3, 2-Mass, DENIS, UCAC2, SDSS, RAVE, Pan Starrs, Hermes, ...) and in two years ESA will launch the Gaia satellite, which will measure astrometric data of unprecedented precision for a billion stars. On account of our position within the Galaxy and the complex observational biases that are built into most catalogues, dynamical models of the Galaxy are a prerequisite full exploitation of these catalogues. On account of the enormous detail in which we can observe the Galaxy, models of great sophistication are required. Moreover, in addition to models we require algorithms for observing them with the same errors and biases as occur in real observational programs, and statistical algorithms for determining the extent to which a model is compatible with a given body of data. JD5 reviewed the status of our knowledge of the Galaxy, the different ways in which we could model the Galaxy, and what will be required to extract our science goals from the data that will be on hand when the Gaia Catalogue becomes available.Comment: Proceedings of Joint Discussion 5 at IAU XXVII, Rio de Janeiro, August 2009; 31 page