Brain regions concerned with perceptual skills in tennis: An fMRI study

Sporting performance makes special demands on perceptual skills, but the neural mechanisms underlying such performance are little understood. We address this issue, making use of fMRI to identify the brain areas activated in viewing and responding to video sequences of tennis players, filmed from the opponent’s perspective. In a block-design, fMRI study, 9 novice tennis players watched video clips of tennis play. The main stimulus conditions were (1) serve sequences, (2) non-serve behaviour (ball bouncing) and (3) static control sequences. A button response was required indicating the direction of serve (left or right for serve sequences, middle button for non-serve and static sequences). By comparing responses to the three stimulus conditions, it was possible to identify two groups of brain regions responsive to different components of the task. Areas MT/MST and STS in the posterior part of the temporal lobe responded either to serve and to non-serve stimuli, relative to static controls. Serve sequences produced additional regions of activation in parietal lobe (bilateral IPL, right SPL) and in right frontal cortex (IFGd, IFGv), and these areas were not activated by non-serve sequences. These regions of parietal and frontal cortex have been implicated in a “mirror neuron” network in the human brain. It is concluded that the task of judgement of serve direction produces two different patterns of response: activations in MT/MST and STS concerned with primarily with the analysis of motion and body actions, and activations in parietal and frontal cortex associated specifically with the task of identification of direction of serve

Self-propelled Bouncing Spherical Robot

25th Annual Denman Undergraduate Research Forum Finalist Second PlaceMost robots that can travel on the ground are either traditional wheeled robots or legged robots. Exploring non-traditional novel robots may provide new solutions for locomotion not previously examined. Currently, self-rolling spherical robots have been designed and manufactured for hobbies, entertainment, or military uses. Similarly, various researchers have built legged robots that walk and run. Our objective in this research project was to design, build, and control a self-propelled bouncing and rolling spherical robot. While some self-bouncing wheeled robots have been built as toys, the self-bouncing spherical robot (one that looks like a ball) remains largely not explored. No one has produced a robot that can bounce continuously and can be steered without any external device to assist its movement. To achieve this goal, we plan to prototype up to three different mechanisms for bouncing. Each prototype would go through brainstorming, computer-aided design and simulation (of the bouncing), initial build, redesign, second build, and final analysis. We follow the classic design cycle: observe, ideation, prototype, and testing. We will also perform dynamic analyses of the robot to improve the design. This thesis reports on current progress towards these goals: we have designed and fabricated (and iterated) on a simple prototype bouncing ball, based on a spinning internal mass; we have performed some 2D and 3D simulations of the spinning mechanism that shows promise for the mechanism to produce persistent bouncing. Future work will consist of improving the current prototype, matching the computer simulations quantitatively to the prototype, performing design optimization and trajectory optimizations for optimal control, exploring other designs closer to hopping robots, and finally, building the ability to control and steer the robot.The Ohio State University Second-year Transformational Experience ProgramThe Ohio State University College of EngineeringNo embargoAcademic Major: Mechanical Engineerin

Similarity solutions in elastohydrodynamic bouncing

We investigate theoretically and numerically the impact of an elastic sphere on a rigid wall in a viscous fluid. Our focus is on the dynamics of the contact, employing the soft lubrication model in which the sphere is separated from the wall by a thin liquid film. In the limit of large sphere inertia, the sphere bounces and the dynamics is close to the Hertz theory. Remarkably, the film thickness separating the sphere from the wall exhibits non-trivial self-similar properties that vary during the spreading and retraction phases. Leveraging these self-similar properties, we establish the energy budget and predict the coefficient of restitution for the sphere. The general framework derived here opens many perspectives to study the lubrication film in impact problems

Elastohydrodynamics of a sliding, spinning and sedimenting cylinder near a soft wall

We consider the motion of a fluid-immersed negatively buoyant particle in the vicinity of a thin compressible elastic wall, a situation that arises in a variety of technological and natural settings. We use scaling arguments to establish different regimes of sliding, and complement these estimates using thin-film lubrication dynamics to determine an asymptotic theory for the sedimentation, sliding, and spinning motions of a cylinder. The resulting theory takes the form of three coupled nonlinear singular-differential equations. Numerical integration of the resulting equations confirms our scaling relations and further yields a range of unexpected behaviours. Despite the low-Reynolds feature of the flow, we demonstrate that the particle can spontaneously oscillate when sliding, can generate lift via a Magnus-like effect, can undergo a spin-induced reversal effect, and also shows an unusual sedimentation singularity. Our description also allows us to address a sedimentation-sliding transition that can lead to the particle coasting over very long distances, similar to certain geophysical phenomena. Finally, we show that a small modification of our theory allows to generalize the results to account for additional effects such as wall poroelasticity

Modeling, analysis and control of robot-object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives

International audienceSo-called robot-object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an "object" and a "robot". Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot-object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article

Feedback Error Learning for Rhythmic Motor Primitives

Abstract — Rhythmic motor primitives can be used to learn a variety of oscillatory behaviors from demonstrations or reward signals, e.g., hopping, walking, running and ball-bouncing. However, frequently, such rhythmic motor primitives lead to failures unless a stabilizing controller ensures their functionality, e.g., a balance controller for a walking gait. As an ideal oscillatory behavior requires the stabilizing controller only for exceptions, e.g., to prevent failures, we devise an online learning approach that reduces the dependence on the stabilizing controller. Inspired by related approaches in model learning, we employ the stabilizing controller’s output as a feedback error learning signal for adapting the gait. We demonstrate the resulting approach in two scenarios: a rhythmic arm’s movements and gait adaptation of an underactuated biped. I

Modelling the dynamics of a sphere approaching and bouncing on a wall in a viscous fluid

The canonical configuration of a solid particle bouncing on a wall in a viscous fluid is considered here, focusing on rough particles as encountered in most of the laboratory experiments or applications. In that case, the particle deformation is not expected to be significant prior to solid contact. An immersed boundary method (IBM) allowing the fluid flow around the solid particle to be numerically described is combined with a discrete element method (DEM) in order to numerically investigate the dynamics of the system. Particular attention is paid to modelling the lubrication force added in the discrete element method, which is not captured by the fluid solver at very small scale. Specifically, the proposed numerical model accounts for the surface roughness of real particles through an effective roughness length in the contact model, and considers that the time scale of the contact is small compared to that of the fluid. The present coupled method is shown to quantitatively reproduce available experimental data and in particular is in very good agreement with recent measurement of the dynamics of a particle approaching very close to a wall in the viscous regime St <O(10), where St is the Stokes number which represents the balance between particle inertia and viscous dissipation. Finally, based on the reliability of the numerical results, two predictive models are proposed, namely for the dynamics of the particle close to the wall and the effective coefficient of restitution. Both models use the effective roughness height and assume the particle remains rigid prior to solid contact. They are shown to be pertinent to describe experimental and numerical data for the whole range of investigated parameters

Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing

Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods have demonstrated impressive learning capabilities using visual input, a lack of tactile sensing limits their capability for fine and reliable control during manipulation. Here we propose a deep RL approach to object pushing using tactile sensing without visual input, namely tactile pushing. We present a goal-conditioned formulation that allows both model-free and model-based RL to obtain accurate policies for pushing an object to a goal. To achieve real-world performance, we adopt a sim-to-real approach. Our results demonstrate that it is possible to train on a single object and a limited sample of goals to produce precise and reliable policies that can generalize to a variety of unseen objects and pushing scenarios without domain randomization. We experiment with the trained agents in harsh pushing conditions, and show that with significantly more training samples, a model-free policy can outperform a model-based planner, generating shorter and more reliable pushing trajectories despite large disturbances. The simplicity of our training environment and effective real-world performance highlights the value of rich tactile information for fine manipulation. Code and videos are available at https://sites.google.com/view/tactile-rl-pushing/.Comment: Accepted by IEEE Robotics and Automation Letters (RA-L