Monthly Archives: June 2017

ONTARIO >> At Woodcrest Junior High School, lunchtime means sandwiches, fresh fruit, robots and circuitry.

Here we go, said seventh-grader Adrian Agustin, 12, tossing a polystyrene ball at a hand-drawn target hooked up to a circuit board. The sensor behind Adrians target registered the impact, and on an attached 8-by-8 grid of lights, an illuminated +1 scrolled across. And another. And so on.

The kids have an opportunity to come in and explore, experiment, hang out with friends, have a safe environment to play with engineering, said Lisa Lista, an instructional coach at the school and one of the two women behind the schools lunchtime STEM (Science, Technology, Engineering and Math) lab.

The lab began in October, sharing space with another classroom, but soon became so popular that it needed a full-sized classroom of its own.

There are times when this classroom is so filled that we need to get help from other teachers, said Sarai Padilla, an instructional coach at the school and Listas partner in the lab.

Up to 30 kids will show up in the laboratory at a time.

I didnt know if kids would come, Lista said. Its during their lunch and their time for hanging out with their friends at the lunch tables. ASB usually has a lot of stuff going on: games, music. But thankfully, a lot of the kids want to come in. They want to do something, hands-on.

The lunchtime STEM lab participants run the gamut from special education students to honors students.

Its a safe place for everybody to come to, Padilla said. You dont have to have any engineering experience at all. They just come in and say Whats this about? and are curious.

The students build Lego Mindstorms robots, HyperDuino computer components and Little Bits modular electronics, like the ones Adrian was using.

Last year, I was using Little Bits in my classroom, with my fifth-graders, and wanted to be able to open up and branch out to other students instead of just my class, Lista said. We wanted a wider range of kids to be able to come.

While there are structured projects available, there are no grades and students are able to experiment and tinker with the labs technology.

We have a girl group who come in and they made an electronic nail file, Lista said, grinning. Its something that theyre interested in. Its easy for them to come in and explore and create.

One student has been so inspired that he and his brother now raid the trash from electronics stores for spare parts.

He has made a Bluetooth earphone set, Padilla said.

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Hes made a fan, Lista said.

... that you plug into your phone, Padilla said.

Hes made a cellphone charger, Lista said. He started here, with Little Bits.

It opened up a new world for him, Padilla said.

Thats awesome, Lista said.

Next year, the STEM lab will be back, sort of.

Its actually going to be a course, Lista said. Next year, its going to be an elective for the students.

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STEM lab serves robotics, computer programming for lunch at Ontario junior high - Inland Valley Daily Bulletin

Apple today announced that its education programming iPad app, Swift Playgrounds, will soon support robots and drones. That means young kids and students will be able to write their own Swift code to control any number of real-world toys and machines. The company is launching the feature next Monday, partnering with a number of top toy and robotics companies including LEGO, automated BB-8 toy maker Sphero, and drone company Parrot. Other companies on board for the launch are toy robot makers UBTECH and Wonder Workshop, as well as Skoog, the maker of a music cube that relies on Swift code to teach children how to compose songs.

Swift Playgrounds, launched last year during Apples 2016 Worldwide Developers Conference, is effectively a video game that teaches kids how to code using Apples Swift programming language. It breaks down how code functions at the most fundamental level and uses colorful environments and visual guides product manager Tim Triemstra even uses the game industry term cutscenes to explain the effects of code and the power of programming. The code appears on the left side of the iPad screen, either automated by the app to teach a lesson or typed in directly by the user, while an animation the code can manipulate plays out on the right.

Kids can now use Swift to send commands to toy robots and drones

Since the Playgrounds launch, Apple has partnered with a number of educational institutions around the country to get Swift built into introductory computer science curriculums and to make its Playgrounds app a fixture in classrooms. The company says Playgrounds has amassed 1 million unique users since launch. When we were designing Swift, from the very first days we wanted it to be everyones first programming language, Triemstra says. We wanted it to be approachable.

Now, with a significant number of Playground users and Swift picking up steam as a lightweight and more elegant way to build iOS apps, Apple is trying to expand its educational focus from software to hardware. Because Playgrounds will support all manner of robotics, including flying drones, the company hopes it will give young kids a whole new reason to engage with programming and learn the secrets of code. It could also do wonders for the popularity of Swift among the next generation of coders, and help cement the language as a fixture for young and eager roboticists.

Given the popularity of success of the Mindstorms robotics series, its a no-brainer that Apple got LEGO onboard for Playgrounds. In a demo at the iPhone makers Cupertino office, a LEGO representative broke down exactly how a Mindstorms EV3 kit can work with Playgrounds, connecting any number of robot-controlling modules to an iPad via Bluetooth. From there, you can see real-time data provided by the robots actuators, motors, and sensors, as well as program commands for fleshed out LEGO bots to receive and carry out. In preparation for the partnership, LEGO says its also designed 10 hours of lessons specifically for the Playgrounds app for kids to run through with a Mindstorms kit.

Similarly, Spheros transparent SPRK+ orb, which is already used to teach kids elements of robotics and programming, will work with the app. Kids will be able to program movements and games for the orb, with exercises breaking down the step-by-step process of development, including a real-world Pong game they can play with their feet as the paddles and the SPRK+ as the ball.

French drone maker Parrot is also buying into Apples new Playgrounds initiative, adding support for its Mambo, Rolling Spider, and Airborne drones. In another demo at Apples offices, a Parrot representative showed off how the Playgrounds app can be used to input commands for the drones to turn, flip in midair, and land in the palm of a users hand.

Apple stresses that none of these new features and hardware tie-ins for Playground are dependent on brand new or tie-in hardware. With LEGO, all you need is a Mindstorms EV3 kit, released as far back as 2013. The same goes for Spheros SPRK+ and Parrots trio of drones so long as you have the supported product, it will sync with Playgrounds and allow you to start controlling it with your own code.

Original post:

Apple's Swift Playgrounds coding app now supports robots, drones, and toys - The Verge

The week is almost over, and so is the 2017 IEEE International Conference on Robotics and Automation (ICRA) in Singapore. We hope youve been enjoying our coverage, which has featured aquatic drones, stone-stacking manipulators, andself-folding soft robots. Well have lots more from the conference over the next few weeks, but for you impatient types, were cramming Video Friday this week with a special selection of ICRA videos.

We tried to include videos from many different subareas of robotics: control, vision, locomotion, machine learning, aerial vehicles, humanoids, actuators, manipulation, andhuman-robot interaction.Were posting the abstracts along with the videos, but if you have any questions about these projects, let us know and well get more details from the authors.

Well return to normal Video Friday next week. Have a great weekend everyone!

This letter presents a physical humanrobot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedancebased controller that adjusts according to the partners performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the users number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects perception of comfort, peace of mind, and robot performance have shown significant difference at the p < .01 level, favoring the PT algorithm.

In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed wholebody grasp planning and motion control method.

Unmanned rescue, observation, and/or research vehicles with high terrain adaptability, high speed, and high reliability are needed in difficult-to-reach locations. However, for most vehicles, high performance over rough terrain reduces the travel speed and/or requires complex mechanisms. We have developed a blade-type crawler robot with a very simple and reliable mechanism, which traverses uneven terrain at high speed. Moreover, the gyro wheel design stabilizes the success of this approach in improving the motion, ensuring robust traversal. The improvement in traveling speed and robustness over uneven terrain by our approach was confirmed by experiment.

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization. To address the second issue, we propose the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.

A 3 DoF parallel cable driven body weight support (BWS) system has been developed for the University of Utahs Treadport Locomotion Interface, for purposes of rehabilitation, simulation of steep slopes, and display of reduced gravity environments. The Treadports large belt (6 by 10 feet) requires a multi-cable support system to ensure that the unloading forces are close to vertical. This paper presents the design and experimental validation, including the system model and force control.

We present a policy search method for learning complex feedback control policies that map from highdimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific instances of a task, and uses these to train a complex, high-dimensional global policy that generalizes across task instances. We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization. We show that these contributions enable us to learn deep neural network policies that can directly perform torque control from visual input. We validate the method on a challenging door opening task and a pick-and-place task, and we demonstrate that our approach substantially outperforms the prior LQR-based local policy optimizer on these tasks. Furthermore, we show that on-policy sampling significantly increases the generalization ability of these policies.

We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed one-way data flow and supports a large number of vehicles per radio. We achieve reliable flight with accurate tracking (< 2cm mean position error) by implementing the majority of computation onboard, including sensor fusion, control, and some trajectory planning. We provide various examples and empirically determine latency and tracking performance for swarms with up to 49 vehicles.

This paper presents a system that consists of three robots to imitate the motion of top volleyball blockers. In a volleyball match, in order to score by spiking, it is essential to improve the spike decision rate of each spiker. To increase the spike decision rates, iterative spiking training with actual blockers is required. Therefore, in this study, a block machine system was developed that can be continuously used in an actual practice field to improve attack practice. In order to achieve the required operating speed and mechanical strength each robot has five degrees of freedom. This robot performs high speed movement on 9 m rails that are arranged in parallel with the volleyball net. In addition, an application with a graphical user interface to enable a coach to manipulate these robots was developed. It enables the coach to control block motions and change the parameters such as the robots positions and operation timing. Through practical use in the practice field, the effectiveness of this system was confirmed.

This paper contributes to quantifying the notion of robotic fitness by developing a set of necessary conditions that determine whether a small quadruped has the ability to open a class of doors or climb a class of stairs using only quasi-static maneuvers. After verifying that several such machines from the recent robotics literature are mismatched in this sense to the common human scale environment, we present empirical workarounds for the Minitaur quadrupedal platform that enable it to leap up, force the door handle and push through the door, as well as bound up the stairs, thereby accomplishing through dynamical maneuvers otherwise (i.e., quasi-statically) unachievable tasks.

We present a simple probabilistic framework for multimodal sensor fusion that allows a mobile robot to reliably locate and approach the most promising interaction partner among a group of people, in an uncontrolled environment. Our demonstration integrates three complementary sensor modalities, each of which detects features of nearby people. The output is an occupancy grid approximation of a probability density function over the locations of people that are actively seeking interaction with the robot. We show empirically that simply driving towards the peak of this distribution is sufficient to allow the robot to correctly engage an interested user in a crowd of bystanders.

Collisions between quadrotor UAVs and the environment often occur, for instance, under faulty piloting, from wind gusts, or when obstacle avoidance fails. Airspace regulations are forcing drone companies to build safer drones; many quadrotor drones now incorporate propeller protection. However, propeller protected quadrotors still do not detect or react to collisions with objects such as walls, poles and cables. In this paper, we present a collision recovery pipeline which controls propeller protected quadrotors to recover from collisions. This pipeline combines concepts from impact dynamics, fuzzy logic, and aggressive quadrotor attitude control. The strategy is validated via a comprehensive Monte Carlo simulation of collisions against a wall, showing the feasibility of recovery from challenging collision scenarios. The pipeline is implemented on a custom experimental quadrotor platform, demonstrating feasibility of real-time performance and successful recovery from a range of pre-collision conditions. The ultimate goal of the research is to implement a general collision recovery solution as a safety feature for quadrotor flight controllers.

State estimation techniques for humanoid robots are typically based on proprioceptive sensing and accumulate drift over time. This drift can be corrected using exteroceptive sensors such as laser scanners via a scene registration procedure. For this procedure the common assumption of high point cloud overlap is violated when the scenario and the robots point-of-view are not static and the sensors field-of-view (FOV) is limited. In this paper we focus on the localization of a robot with limited FOV in a semi-structured environment. We analyze the effect of overlap variations on registration performance and demonstrate that where overlap varies, outlier filtering needs to be tuned accordingly. We define a novel parameter which gives a measure of this overlap. In this context, we propose a strategy for robust non-incremental registration. The pre-filtering module selects planar macro-features from the input clouds, discarding clutter. Outlier filtering is automatically tuned at run-time to allow registration to a common reference in conditions of non-uniform overlap. An extensive experimental demonstration is presented which characterizes the performance of the algorithm using two humanoids: the NASA Valkyrie, in a laboratory environment, and the Boston Dynamics Atlas, during the DARPA Robotics Challenge Finals.

In this paper, we propose an epoch-making soft sheet actuator called Wavy-sheet. Inspired by gastropods locomotion, Wavy-sheet can generate continuous traveling waves on the whole soft body. It aims to be applied to a mobile soft mat capable of moving and transporting without damaging the object and the ground. The actuator, driven by pneumatics, is mainly composed of a couple of flexible rubber tubes and fabrics. The advantages are: i) many traveling waves can be generated by just three tubes, ii) the whole structure can adapt its own shape to the outer environment passively, and iii) only 10 mm in thickness and can generate waves with larger than 10mm in amplitude. In this paper, first, we describe the basic concept of Wavy-sheet, and then show the configuration and the principle of wave propagation. Next, fabrication methods are illustrated and the design methods are addressed. By using a prototype actuator, several experiments are conducted. Finally, we verify the effectiveness of the proposed actuator and its design methods.

Part handling in warehouse automation is challenging if a large variety of items must be accommodated and items are stored in unordered piles. To foster research in this domain, Amazon holds picking challenges. We present our system which achieved second and third place in the Amazon Picking Challenge 2016 tasks. The challenge required participants to pick a list of items from a shelf or to stow items into the shelf. Using two deep-learning approaches for object detection and semantic segmentation and one item model registration method, our system localizes the requested item. Manipulation occurs using suction on points determined heuristically or from 6D item model registration. Parametrized motion primitives are chained to generate motions. We present a full-system evaluation during the APC 2016 and componentlevel evaluations of the perception system on an annotated dataset.

For collaborative robots to become useful, end users who are not robotics experts must be able to instruct them to perform a variety of tasks. With this goal in mind, we developed a system for end-user creation of robust task plans with a broad range of capabilities. CoSTAR: the Collaborative System for Task Automation and Recognition is our winning entry in the 2016 KUKA Innovation Award competition at the Hannover Messe trade show, which this year focused on Flexible Manufacturing. CoSTAR is unique in how it creates natural abstractions that use perception to represent the world in a way users can both understand and utilize to author capable and robust task plans. Our Behavior Tree-based task editor integrates high-level information from known object segmentation and pose estimation with spatial reasoning and robot actions to create robust task plans. We describe the crossplatform design and implementation of this system on multiple industrial robots and evaluate its suitability for a wide variety of use cases.

In this paper, we present the mechatronic design of our Tactile Omnidirectional Robot Manipulator (TOMM), which is a dual arm wheeled humanoid robot with 6DoF on each arm, 4 omnidirectional wheels and 2 switchable end-effectors (1 DoF grippers and 12 DoF Hands). The main feature of TOMM is its arms and hands which are covered with robot skin. We exploit the multi-modal tactile information of our robot skin to provide a rich tactile interaction system for robots. In particular, for the robot TOMM, we provide a general control framework, capable of modifying the dynamic behavior of the entire robot, e.g., producing compliance in a non-compliant system. We present the hardware, software and middleware components of the robot and provide a compendium of the base technologies deployed in it. Furthermore, we show some applications and results that we have obtained using this robot.

We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified secondorder update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robots end effector.

Soft compliant materials and novel actuation mechanisms ensure flexible motions and high adaptability for soft robots, but also increase the difficulty and complexity of constructing control systems. In this work, we provide an efficient control algorithm for a multi-segment extensible soft arm in 2D plane. The algorithm separate the inverse kinematics into two levels. The first level employs gradient descent to select optimized arms pose (from task space to configuration space) according to designed cost functions. With consideration of viscoelasticity, the second level utilizes neural networks to figure out the pressures from each segments pose (from configuration space to actuation space). In experiments with a physical prototype, the control accuracy and effectiveness are validated, where the control algorithm is further improved by an optional feedback strategy.

This paper presents a systematic approach for the 3-D mapping of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. In addition to the underwater vision constraints, cave mapping presents extra challenges in the form of lack of natural illumination and harsh contrasts, resulting in failure for most of the state-ofthe-art visual based state estimation packages. A new approach employing a stereo camera and a video-light is presented. Our approach utilizes the intersection of the cone of the video-light with the cave boundaries: walls, floor, and ceiling, resulting in the construction of a wire frame outline of the cave. Successive frames are combined using a state of the art visual odometry algorithm while simultaneously inferring scale through the stereo reconstruction. Results from experiments at a cave, part of the Sistema Camilo, Quintana Roo, Mexico, validate our approach. The cave wall reconstruction presented provides an immersive experience in 3-D.

This paper investigates how a robot that can produce contingent listener response, i.e., backchannel, can deeply engage children as a storyteller. We propose a backchannel opportunity prediction (BOP) model trained from a dataset of childrens dyad storytelling and listening activities. Using this dataset, we gain better understanding of what speaker cues children can decode to find backchannel timing, and what type of nonverbal behaviors they produce to indicate engagement status as a listener. Applying our BOP model, we conducted two studies, withinand between-subjects, using our social robot platform, Tega. Behavioral and self-reported analyses from the two studies consistently suggest that children are more engaged with a contingent backchanneling robot listener. Children perceived the contingent robot as more attentive and more interested in their story compared to a non-contingent robot. We find that children significantly gaze more at the contingent robot while storytelling and speak more with higher energy to a contingent robot.

In this paper, we show that visual servoing can be formulated as an acceleration-resolved, quadratic optimization problem. This allows us to handle visual constraints, such as field of view and occlusion avoidance, as inequalities. Furthermore, it allows us to easily integrate visual servoing tasks into existing whole-body control frameworks for humanoid robots, which simplifies prioritization and requires only a posture task as a regularization term. Finally, we show this method working on simulations with HRP-4 and real tests on Romeo.

For the past few years, unmanned aerial vehicles (UAVs) have been successfully employed in several investigations and exploration tasks such as aerial inspection and manipulations. However, most of these UAVs are limited to open spaces distant from any obstacles because of the high risk of falling as a result of an exposed propeller or not enough protection. On the other hand, a UAV with a passive rotating spherical shell can fly over a complex environment but cannot engage in physical interaction and perform power tethering because of the passive rotation of the spherical shell. In this study, we propose a new mechanism that allows physical interaction and power tethering while the UAV is well-protected and has a good flight stability, which enables exploration in a complex environment such as disaster sites. We address the current problem by dividing the whole shell into two separate hemispherical shells that provide a gap unaffected by passive rotation. In this paper, we mainly discuss the concept, general applications, and design of the proposed system. The capabilities of the proposed system for physical interaction and power tethering in a complex space were initially verified through laboratory-based test flights of our experimental prototype.

We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the largescale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.

We present the design, modeling, and implementation of a novel pneumatic actuator, the Pneumatic Reel Actuator (PRA). The PRA is highly extensible, lightweight, capable of operating in compression and tension, compliant, and inexpensive. An initial prototype of the PRA can reach extension ratios greater than 16:1, has a force-to-weight ratio over 28:1, reach speeds of 0.87 meters per second, and can be constructed with parts totaling less than $4 USD. We have developed a model describing the actuator and have conducted experiments characterizing the actuators performance in regards to force, extension, pressure, and speed. We have implemented two parallel robotic applications in the form of a three degree of freedom robot arm and a tetrahedral robot.

Humans utilize their torsos and arms while running to compensate for the angular momentum generated by the lower-body movement during the flight phase. To enable this capability in a humanoid robot, the robot should have human-like mass, a center of mass position, and inertial moment of each link. To mimic this characteristic, we developed an angular momentum control method using a humanoid upper body based on human motion. In this method, the angular momentum generated by the movement of the humanoid lower body is calculated, and the torso and arm motions are calculated to compensate for the angular momentum of the lower body. We additionally developed the humanoid upper-body mechanism that mimics the human link length and mass property by using carbon fiber reinforced plastic and a symmetric structure. As a result, the developed humanoid robot could generate almost the same angular momentum as that of human through human-like running motion. Furthermore, when suspended in midair, the humanoid robot produced the angular momentum compensation in the yaw direction.

IEEE Spectrums award-winning robotics blog, featuring news, articles, and videos on robots, humanoids, drones, automation, artificial intelligence, and more. Contact us:e.guizzo@ieee.org

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Video Friday: Robot Dance Teacher, Transformer Drone, and Pneumatic Reel Actuator - IEEE Spectrum