Teaching humanoid robotics by means of human teleoperationthrough RGB-D sensorsStefano Michieletto ∗, Elisa Tosello, Enrico Pagello, Emanuele MenegattiIntelligent Autonomous Systems Lab (IAS-Lab), Department of Information Engineering (DEI), University of Padova, Italy1
h i g h l i g h t s
• A graduate course project on humanoid robotics is presented.• Humanoids are used for a Project-Based Learning (PBL) constructivist approach.• The task combines teleoperation with an integrated programming framework.• Practical and theoretical aspects are described looking at learning objectives.• An analysis is performed on how students autonomously solve problems in groups.
This paper presents a graduate course project on humanoid robotics offered by the University of Padova.The target is to safely lift an object by teleoperating a small humanoid. Students have tomap human limbsinto robot joints, guarantee the robot stability during the motion, and teleoperate the robot to performthe correct movement.
We introduce the following innovative aspects with respect to classical robotic classes: i) the use ofhumanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploitingthe potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach asteaching methodology.
The learning objectives of both course and project are introduced and compared with the students’background. Design and constraints students have to deal with are reported, together with the amount oftime they and their instructors dedicated to solve tasks. A set of evaluation results are provided in orderto validate the authors’ purpose, including the students’ personal feedback. A discussion about possiblefuture improvements is reported, hoping to encourage further spread of educational robotics in schoolsat all levels.
The complexity of building and programming humanoid robotsmake university students in engineering courses interested instudying these platforms [1]. Interfacing with humanoids allowsthe implementation of intelligent, stable, and balanced multi-Degrees Of Freedom (DOF) movements fixing complex issues likerobot stability, multi-limb coordination, and high-DoFs inverse
∗ Corresponding author.E-mail addresses:[email protected] (S. Michieletto),
kinematics. Other types of robots, like the wheeled ones, do notoffer these features. However, the high cost of these automata,the efforts required to maintain their proper functioning, andthe knowledge required to deal with them make their usage asteaching materials a not yet widespread proposal.
The ‘‘Autonomous Robotics’’ (AR) course of the Master ofScience (MSc) in Computer Science of the University of Padovaat the Intelligent Autonomous Systems Laboratory (IAS-Lab) [2]proposes a humanoid robotics teaching project asking studentsto stable move a robot in order to grasp an object. The projectassigns students three different tasks: mapping human limbs intorobot joints, maintaining the robot stability during the motion,and teleoperating the robot to perform the correct movement. Theproject subdivision guarantees students to deal with problems ofincreasing complexity.
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The University of Tokyo uses a very expensive humanoid robot,the HRP2 [3]. The Shibaura Institute of Technology proposed the E-Nuvo [4],which is less expensive than theHRP2.Nevertheless, bothinstruments are still costly and can only be used by experiencedusers. Other experiments were conducted to lower costs andmakehumanoid robots affordable to universities to use them as teachingmaterials. An example is [5], which fabricated robot systems usingeasily available cheap key components; a servomotor of a toy anda PICmicrocomputer, for example. The system is cheap but lackingbecause it is composed of only robot’s lower limbs. This is usefulfor a beginner to fabricate his or her first control program, butthe study of the robot’s stability is oversimplified. Our sectionsprovide students with two cheap and popular humanoid robots:the Vstone Robovie-X [6], a 17 DOF robots suitable for first timerobotics builder, and the Aldebaran NAO (Fig. 1), a platform with25 DOF, vision, audio, and tactile sensors, usable also by advancedusers.
In order to facilitate students solving the assignment, weprovide them with a robotic framework, ROS (Robot OperatingSystem) [7], which collects themost popular robotics libraries. Theuse of ROS is spreading among the world-wide robotics coursesand various robotics challenges foresee its usage to solve serviceor industrial robotics problems (e.g., the ROCKIn@Home [8] and@Work [9] challenges, or the RoboCup@Home and @Work [10]challenges). They do not strictly require humanoid robots toperform tasks. Other challenges, focused on humanoids (e.g. theHUMABOT [11], RoboCup Soccer [10] and DARPA [12] roboticschallenges), face challenging robotic problems like robot stability,manipulation or grasping; however, only selected deservedstudents can participate. We aim to offer even beginning studentsthe chance to interface with humanoid robots, giving them thenecessary background to face, at a latter time, European orworldwide robotics competitions.
Another key factor that distinguishes our teaching approachfrom others that use humanoids, e.g., [13], is teleoperation [14].We simplify the stable locomotion resolution giving students thepossibility to teleoperate the robot. Students can compare theirmovements with the robot ones, and take advantage of similaritiesto solve the motion planning problem in a natural ‘‘human’’way.They can first analyze the human motion, understand how totranslate it with respect to the robot constraints, and finally solvethe robot stability problem.
We adopt the project-based learning (PBL) [15] teachingmethodology, based on the Papert’s perspective of Construc-tivism [16–18]. The course consists of theoretical and practicallessons. Classes give students a background on robotics funda-mentals; laboratories let them to solve known robotics problemsexploiting the acquired theoretical knowledge. In labs, students or-ganize themselves in teams and face problems alone freely choos-ing the resolution method. Students can collaboratively discuss,reflect, exchange ideas, and combine each other’s techniques toachieve better solutions. In this way, they develop inquiry, inves-tigation, and collaboration skills, in turn, increasing overall com-prehension of the issues [19,20]. It is the opposite of traditionalclassrooms embracing the cognitive approach from Neisser [21]:students receive knowledge passively and work primarily alone,learning is achieved through repetition, and subjects are strictlyadhered to and are guided by textbooks [22]. Only a few shortlaboratory experiences are assigned, usually consisting of prede-termined instructive sequences solving very particular and sim-plified real cases. Other robotics teachings adopt the construc-tivist methodology. An example is the TERECoP project [23].The difference is that institutions joining this project base theirteaching only on the LEGO Mindstorms kit [24]. Capabilities ofMindstorms robots are limited and students cannot test their pro-gramming abilities solving complex problems. Our labs have an
increasing difficulty. At first, two experiences are assigned deal-ing with LEGO robots; then, the humanoid project is presented. Asa consequence, students first solve simple, but not trivial, prob-lems [25]. Then, they become comfortable with robotics and candeal with humanoids. Also the above mentioned robotics chal-lenges adopt the PBL constructivist approach as teaching method-ology but, as stated before, our humanoid project aims to increasethe robotics knowledge of all students enrolled in the course andnot to be a practice reserved to some of them.
The rest of the paper is organized as follows. Section 2 describesthe technical details of the project (e.g. schedule, goals, evaluationmethods). Section 3 summarizes the results proposed to solve theassigned tasks. Section 4 discusses the outcomes of the projectduring the 2011/2012, 2012/2013, and 2013/2014 academic years.In Section 4, some conclusions and future perspectives aredescribed.
2. Humanoids teleoperation project
‘‘Autonomous Robotics’’ (AR) is a second year course of theMaster of Science (MSc) in ‘‘Computer Science’’ at the Faculty ofEngineering of the University of Padova (Italy). It offers studentsmethodological bases for programming autonomous robotics sys-tems combining theoretical class lectures and practical laboratoryexperiences. The former aims to build a strong background onrobotics fundamentals, perception systems, computer vision, andnavigation; the latter allows students to learn how to use roboticsalgorithms and software.
2.1. Schedule
The course lasts 12 weeks and is composed of three lessons oftwo hours perweek. Every twoweeks, during the class, the teacherpresents a laboratory experience that students have to solve usingthe theoretical foundation of previous lessons.
To solve labs, students organize themselves in teams oftwo/three (classes of about 20 students per year). Larger groupsinduce confusion and unbalanced workload division within thegroup itself. One teacher attends the labs and supervises andhelps students when in doubt. No more teachers are requiredto successfully complete tasks. In fact, the instructor does notslavishly guide students on the fulfillment of assigned tasks;instead, during the class students learn what the goals are, andthey try to solve problems discussing within the teams. Studentscan every day access the laboratory and use the available robots.The only constraint is presenting the solutionswithin threemonthsfrom the end of the course.
2.2. Goals
The humanoids teleoperation project asks students to safely liftan object by teleoperating a small humanoid. It is composed ofthree experiences: (1)motion remapping:maphumanmovementsinto robot ones by using teleoperation (2) robot stabilization:teleoperate the robot in order to stable pick up an object (no robot’stilting desired) (3) motion planning: plan robot motion on clutterenvironments. No specific algorithm is requested to accomplishthe assigned tasks: students can implement a method taught inthe theoretical lessons, adopt a library alreadyprovidedwithusefulalgorithms, look for a different state of the art technique by readingscientific articles, or even develop a their own novel concept.Tasks have to be solved using ROS and tested on the real andsimulatedmodels of the Vstone Robovie-X and the AldebaranNAO.Gazebo [26] is adopted as simulation environment. An itemizeddescription of the three sub-parts follows. It includes a briefdescription of every task, the theoretical knowledge required toface it, and the robotic and computer science objectives inferredfrom its successful completion and validation. More details can befound on the Lab website [2].
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(a) Vstone Robovie-X. (b) NAO.
Fig. 1. The small humanoid used in this work: Aldebaran NAO and Vstone Robovie-X.
2.2.1. Experience 1: motion remappingStudents have to map human joints into that of a Robovie-X
robot. They have to record the humanmotion using a RGB-D sensor(we select a Microsoft Kinect) and track the human skeleton usinga skeletal tracking system (namely NiTE [27]). Skeleton framesmust be mapped into robot joints and published over tf [28] tocontrol the robot. The ROS tf package lets the user to keep trackof multiple coordinate frames over time; e.g., the hip, knee, andankle reference systems. Simultaneously controlling these systemslets the generation of a robot motion as similar as possible to thehuman one.
Theoretical background: Robot Learning from Demonstration(RLfD) [14,29] lets a system to learn a task performed by ahuman demonstrator and reproduce it through a robot. A RLfDframework [30] is available to students and can be taken asexample. It uses an RGB-D sensor to acquire the scene (humanin action). A skeleton tracking algorithm extracts the usefulinformation from the acquired images (positions and orientationsof skeleton joints); and this information is given as input to themotion re-targeting system that remaps the skeleton joints intothat of a manipulator robot. After the remapping, a model forthe robot motion controller is retrieved by applying a GaussianMixture Model (GMM) and a Gaussian Mixture Regression (GMR)on the collected data.
Robotics objectives: The experience involves motion control, online data elaboration and reaction, human–robot interaction, andteleoperation. Students have to analyze human movements andtranspose them to the robot DOFs dealing with the differencesbetween these complex motion systems. They learn to useadvanced ROS modules (e.g., the transformations and frames (tf )package) and to change the reference system while maintainingthe fundamental rototranslation constraints. They have to evaluate
robot characteristics in both virtual and real environment in orderto obtain a good approximation of human movements. At thispreliminary stage, students do not have to consider the robotstability: the robot is fixed to a bracket letting robot limbs to movewithout stability limitations.
Computer science objectives: Students learn to handle largeamounts of data: RGB-D sensors provide RGB and depth images athigh frame-rate (30 fps), and the skeleton tracking systemprovidesthe joints values recorded at every instant. Students should be ableto elaborate the raw data while maintaining an elevated frame-rate in the robot control process. Moreover, they learn to use anobject oriented approach: in order to control the robot, studentshave to publish its joints values using the ROS publisher/subscribercommunication protocol learned in the LEGO experiences [25].
2.2.2. Experience 2: robot stabilizationThe goal of this experience is tomake a Robovie-X robot picking
up an object by means of human teleoperation. Using the systemdeveloped in Experience 1, students have to record the humanmovements necessary to pick up the object and use them tocommand the robot. Robot’s tilt and fall must be avoided: thetheory learned in class about the robot stability control must beapplied in order to balance input data. Actions must be performedin real-time: the human has to real-time teleoperate the robot.
Theoretical background: In balancing control, the robot’s Zero-Moment-Point (ZMP) [31] is the most important factor inimplementing stable bipedal robot motions. If the ZMP is locatedin the region of supporting sole, then the robot will not fall downduring motions. Moreover, to ensure a stable walk, the robot’sCenter of Mass (CoM) must maintain the same height during
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locomotion movements [32]. Assume that the motion of CoM isconstrained on the surface z = cz , that c = [cx, cy, cz]T is theposition of CoM, and the ZMP is described by the position on theground p = [px, py, 0]T . Then:
px = cx −czgc̈x = cx −
zgc̈x
py = cy −czgc̈y = cy −
zgc̈y
where g is the acceleration of gravity. During the execution of theteleoperated movements, the ZMP must be inside the supportingsole (pz = 0) and the CoM does not have to perform a movementparallel to the ground, it has to perform a trajectory perpendicularto the ground. This means x = xc, y = yc . The ZMP positionfollows.
Robotics objectives: The aim of this experience is to face therobot stabilization problemmoving a humanoid robot as a human.Students’ algorithms have to elaborate a feedback signal able tocorrect joints values and stabilize the robot.
Computer science objectives: This experience allows students toapply concepts learned during previous experiences in a differentenvironment in order to consolidate them.
2.2.3. Experience 3: motion planningStudents have to plan the motion of an Aldebaran NAO robot
in a 2D simulated environment populated by obstacles. The robothas to walk through a prefix path avoiding collisions with otherobjects around it. Students can also challenge themselves in otherscenarios: 3D environments and dynamic maps. They can also usereal robots.
Theoretical background: A motion planning problem aims toproduce a continuous sequence of collision-free robot configura-tions connecting a start configuration S to a goal configurationG. The robot and obstacle geometry is described in a 2D or 3Dworkspace, while the motion is represented as a path in the con-figuration space. The Open Motion Planning Library (OMPL) [33]is the most commonly used collection of motion planning algo-rithms. It implements the basic primitives of sampling-based mo-tion planningwhich, instead of computing the exact solution of theproblem, sample the states space of the robot. Examples of avail-able OMPL planners are Probabilistic Roadmap Method (PRM) andRapidly-exploring Random Trees (RRT) [34].
Robotics objectives: At the end of the experience the robot must(at least) walk in a simulated environment without colliding withthe obstacles populating the scene. This means students have to beable to construct a 2D (or 3D) map, they must have a theoreticalknowledge of the motion planning algorithms presented in class,and they must be able to apply them to find a free path froma starting to a final configuration. The experience gained duringthe second project’s assignment must be applied to guarantee therobot’s stability during the motion.
Computer science objectives: This experience is also aimedto consolidate Computer science objectives learned during thecourse.
2.3. Evaluation
Project marks are E (not submitted), D (failed), C (mandatory),B (good), and A (very good). Each of the three laboratoryexperiences is worth one third of the final project grade,and for every sub-part students must deliver the source-codeand a report. Starting from the 2013/2014 Academic Year, avideo demonstrating the proper functioning of the designedimplementation is also required to be loaded in the Lab’s YouTubechannel. Students evaluation is based on the technical writing,e.g., document organization, comprehensiveness, style, references,
and synthesis, and on the complexity and originality of theapproach used, e.g., they are rewarded when organizing softwareinto modules, reusing data structures and classes, exploiting classinheritance. All skills are necessary for any engineer. Marks of thethree experiences are assigned as follows.
2.3.1. Experience 1: motion remappingE: report not submitted;D: the robot does notmove; e.g., the tracking systemdoes not track
human joints, human and robot joints do not match, the ROSpublisher node does not publish the tf values necessary tomovethe robot;
C: the robotmoves but its joints do not preciselymatch the humanones; e.g., robot movements do not exactly reproduce humanones;
B: the robot moves and joints match. However, the report isnot well organized and does not clearly describe the adoptedapproach;
A: the robot moves precisely reproducing human movements andthe report well described the adopted approach.
2.3.2. Experience 2: robot stabilizationE: report not submitted;D: the robot fall; e.g., no stabilization rule is applied or the
stabilization feedback signal is incorrectly implemented;C: the robot performs the movement but swings; e.g., only not
justified manual corrections are applied to stabilize the robot;B: the robot swings but the adopted stabilization approach is well
described and reflects the explained theoretical background.In this case, usually students adopt corrective coefficients thatdo not consider the hardware characteristics of the robot(e.g., joints engines could not be equally calibrated);
A: the adopted control approach makes the system stable.+: the robot stably fulfills other actions in addition to the picking
one.
2.3.3. Experience 3: motion planningE: report not submitted;D: no 2D map of the environment is built, the robot does not
navigate;C: themap is built, the robot can navigate but no path is computed
to guide the robot from S to G;B: the path is computed but collides or forces the robot to go
around in circles;A: the path is computed, it does not collide and is optimal.
A detailed report describes the algorithm used to solve themotion planning problem.
+: tests are performed on a real robot or on a 3D environment.
3. Results
Readers can view student results visiting the linkhttps://www.youtube.com/playlist?list=PLvyUNGk1lOSUpE1h14gFVFzX9OhPUPO08 . We summarize them by analyzing the threeprojects’ sub-parts.
3.0.4. Experience 1: motion remapping
The mapping experience has been mainly faced by using twomethods. The first one matches each robot joint with that ofthe human counterpart and computes the angle of every joint.The resulting values are used to properly move the robot. Thematching phase could be very tricky because the robot kinematicchain could be very different from the human one, especially insome angle limits. Some students looked for the maximum andminimum of each selected human joint by testing several subjects;then, they scaled the computed joint values according to thelimitation imposed by the robotic platform. Other groups had toface a singularity problem in the selected mapping and proposed
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an hysteresis system to prevent rapid switching of configurationin the humanoid due to sensor noise. This method usually resultsvery natural to users, on the other hand it is not so precise becausea small error in an angle could correspond to a huge change inposition. A second method has been used to avoid this behavior:inverse kinematics. Students identified a sort of end effector foreach limb and computed the joint angles in order to obtain similarpositions between human and robot. Nevertheless, the similarity islimited to the end effector position while other joints can assumevery different configurations with respect to the human body, soin some cases the robot motion seems quite unnatural to users.During the academic year 2011/2012, a team tried to overcomelimitations of the already presented methods by mixing them ina hybrid solution. Like in the first method, to ensure the motion tobe as natural as possible, they fixed some joint angles by matchinghuman and robot joints. While the remaining ones are computedby means of inverse kinematics in order to reach a good precisionof the end effector positions. The result has proven to be quiteeffective and it has been widely adopted by almost all the studentsin the following years.
3.0.5. Experience 2: robot stabilization
More various solutions have been proposed for the stabilityproblem. Inmost cases, the final goal is to keep the CoM projectionof the robot inside its ground contact area. A very simple solutionconsidered hip (α), knee (β), and ankle (γ ) joints (Fig. 2) to imposea strong relation between the three joints (i.e. γ = α =
β
2 ).Some refinements have also been applied to this basic idea in orderto involve all the lower body joints and at the same time adaptthem to a natural human behavior. More complex solutions tookinto account the entire structure of the robot in order to computeat each instant the ground projection of the CoM (CoMx, CoMy)and maintain it in a safe region. Dynamic methods involved thegyroscope to measure the robot inclination and balance it byapplying an appropriate motion. In this solution, the platform hasbeen modeled as an inverted pendulum. The stability has beenguarantee by compensating forces causing the robot fall with anopposite movement of the torso. This technique is particularlysuited for the picking up phase, in which the object mass has tobe considered to reach the stability. A symmetric motion of therobot limbs or the definition of a safe position in case of failedtracking have been used as minor expedients to obtain a betterhuman–robot interaction.
3.0.6. Experience 3: motion planning
To solve this part, students have to understand the connectionbetween the virtual robot model and the algorithm performingthe planning. They built their own map and made the robotnavigate from a point to another within it. Most students used theRRT algorithm of the OMPL library to solve the motion planningproblem.Depending on themap, they found the proper parametersin order to reach the goal even when dealing with complexpaths. Some groups have also built a map representing a realenvironment, while some others have tested the navigation usingthe real NAO platform.
4. Discussion
In this section authors discuss the results obtained duringseveral academic years. An analysis is proposed regarding themarks that students received in both the humanoid project andthe overall course. A questionnaire is reported in order to analyzestudent feedback.
Fig. 2. Joint angles involved in a simple method to maintain the robot stability.
4.1. Project marks
Fig. 3 shows the distribution of student marks according tothe weighting specified in Section 2.3. Marks are subdividedaccording to the academic year and to the three projects’ sub-parts. Marks are generally high: 68.6% of all students obtained themaximum mark, and only 1% delivered works meeting only themandatory requirements. Teams usually fulfilled the requirementsby showing a good analysis of their work and often accomplishingoptional tasks.
In 2013/2014 Academic Year, the percentage of A obtained inthe ‘‘Planningëxperience is smaller than that of previous years.This is justified by the two weeks shift of the laboratory schedule.The laboratory schedule was moved forward due to Italian publicholidays, but the exam session started as usual. Several studentsdecided not to perform optional tasks to better prepare otherexams.
Another important aspect to notice is the lack of increasinggrades during the three stages. The use of a completely differentrobot structurewith a higher number of Degrees of Freedom (DoFs)did not affect students’ capabilities to fulfill the requirements. Thereasons of the achievement are manifold. The main one is theadoption of ROS: its use lead students to develop algorithms in astructured environment.
The high percentage of A confirms that combining a construc-tivist approach with the assignation of tasks of increasing com-plexity leads to the desired results: according to the assigned task,students become able to autonomously select the better resolutionapproach and to precisely justify themade decisions. An example isthe solution presented to face themapping sub-task. Students haveto guarantee both natural robotmotions and good precision. No al-gorithm has been indicated to accomplish the task. Therefore, theyhave to look for possible solutions, their features and to choose theoption they suppose to better suit the requirements. They also haveto face the consequences of their choice, and a random techniqueis quite rarely the best solution. Moreover, students are asked tolist themotivations of their choices, the problems encountered andthe possible sources of these problems. This practice helps them tounderstand the relevance of the selected methods with respect tothe achieved results, relate each algorithm to the theory, and im-plement it for the project. It could be seen as a virtuous circle inwhich real problems are faced by putting theoretical concepts intopractice, while a solid knowledge of the theory is obtained throughpractical applications.
4.2. Course marks
The final exam of the Autonomous Robotics course consists ofa final project in which students have to deeply examine a specific
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(a) 2011/2012. (b) 2012/2013.
(c) 2013/2014. (d) Overall.
Fig. 3. Percentage of students with respect to the mark achieved in each part of the project mapping (green), stability (blue), and planning (yellow). The academic years2011/2012 (a), 2012/2013 (b), 2013/2014 (c), and the overall students attending the course in the three years (d) have been considered.
argument. The topic of the project is selected in accordance witha tutor (a professor, a post-doc, or a Ph.D. student) affiliated to theIntelligent Autonomous Systems Lab. It usually involves topics thatstudents learned in class.
Students are asked to provide a small report illustrating thestate of the art of the selected argument and how they improvedit. The expected result is similar to a scientific paper. They arealso asked to give an oral presentation lasting 20 min (15 min forpresentation and5min for questions) explaining theproject. In thiscase, the marks go from 0 to 30 for historical reasons in the ItalianUniversity. With marks under 18 the exam is considered failed,while a ‘‘laude’’ can be assigned to excellent students, showingparticularly brilliant results during all the course. The humanoidteleoperation project assigns a maximum of 3 point (10%) in thefinal grade and it is mandatory.
The graph in Fig. 4 reports the percentage of students havinggrades from 18 to 30 cum laude during the three academic yearsconsidered in this work.
The good trend persists. The good practices learnt from theassigned projects (the one concerning mobile robots [25] and theone presented in this paper) helped students. They have been ableto better understand the theoretical lessons and to properly collectthe knowledge necessary to build up their own robotic project.
4.3. Student questionnaire
At the end of the course, students were asked to fill ananonymous questionnaire. The aimwas to verify the correct designof the course itself. Questions of Table 1were posed. The answer toeach question is represented by a choice among four states: Not atall (yellow), A little (red), Enough (blue) and Very much (green).
The questionnaire was meant to test key aspects of thelaboratory activity:
– comprehension of concepts analyzed during the mobile robots’labs;
– effort spent in switching to a more complicated robot with alack of sensors;
– closeness within the two activities and with possible futurejobs.
Answers to the questionnaire highlight similar results for allthe considered academic years. The effectiveness of the adoptedmethod is confirmed, even by using a more articulated robotlike an humanoid (Question 4). Students were able to assimilateknowledge gained by using a mobile robot and to apply itin a different manner during the following experiences, beingaware of the gradually increasing complexity of the proposedtasks (Question 1). The elevate number of DOFs in humanoidrobots forced them to change their approach to robot control(Question 3) drawing inspiration from the similarities betweenhumanoids and humanmotion, but even looking at the differencesbehind appearances. Students had also to balance the lack ofsensors mounted on the robot by estimating the CoM of thehumanoid while teleoperating it through human motion. Facingthis complexity make them conscious of the importance ofperception in robotics (Question 2) and enable a critical analysisof possible solutions when data are missing (Question 5). Finally,the adoption of a constructivist approach in teaching roboticscombined with an high level robotics framework emphasize theuse of new problem solving methodologies in a new class ofyoung, versatile engineers entering the job market in few months(Question 6).
5. Conclusions
This paper presented a series of experiences based on aconstructivist approach and targeted M.Sc students attendingthe ‘‘Autonomous Robotics’’ course. Experiences focused oncontrolling movements and stability of a humanoid robot. Theserobot skills can be seen as a small but complete set of abilitiesstudents should gain to deal with humanoid robots.
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(a) 2011/2012. (b) 2012/2013.
(c) 2013/2014. (d) Overall.
Fig. 4. Percentage of studentswith respect to themark achieved in the entire Autonomous Robotics course. The academic years 2011/2012 (a), 2012/2013 (b), 2013/2014 (c),and the overall students attending the course in the three years (d) have been considered.
Table 1Results of the questionnaire.
2011/2012 2012/2013 2013/2014
1 The complexity of the experiences has increased with the adoption ofhumanoid robots in place of mobile platforms.
2 Lack of sensors in Robovie-X platform affects robot performances
3 The Robovie-X high number of DOFs with respect to LEGO Mindstorm NXTaffected the approach adopted in controlling the robot.
4 Using humanoid robots is the natural extension of the work started withmobile robots.
5 Using humanoid robots gives another point of view about robotics withrespect to mobile robots.
6 In my future job I will be asked to work with modular software structuressimilar to ROS.
Legend: Not at all A little Enough Very much
Students were asked to control the robot motion and stabilityby means of human teleoperation instead of analytically solvingthe robot inverse kinematics and dynamic. The approach makesstudents able to face the problem from a more natural point ofview. The correct resolution of the assigned problems and thepositive students’ feedback give instructors the certainty thatcombining Constructivism with a gradual increase of the level ofdifficulty is effective in teaching robotics.
Our goal for the future is to expand the teaching frameworkincluding new sensors and functionalities. The expansion willmake the course a solid foundation that will train studentsfor the robotics working world or for challenging experienceslike robotics competitions. Students will deepen their roboticsknowledge andwill be increasingly involved and proactive towardrobotics, a discipline that brings together a wide range of fields,from technology to design, frommathematics to science education.
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StefanoMichieletto is Post-Doc at IntelligentAutonomousSystems Laboratory (IAS-Lab) at the University of Padova.He received his Ph.D. in Science and Technology Informa-tion at the University of Padova, Italy, in 2014. He gradu-ated in Computer Science in 2008 at the same university.His research interests are in the field of machine learning,robot programming by demonstration, manipulators, andhuman–robot interaction using RGB-D sensors.
Elisa Tosello is a third year Ph.D. student at the Schoolof Engineering of the University of Padova. She belongsto the Intelligent Autonomous System Laboratory (IAS-Lab) research group. She received her B.Sc. (2009) andM.Sc. (2012) in Computer Engineering at the Universityof Padova. Her research focuses on the Hardware andSoftware reuse for the resolution of the Motion Planningproblem for multi-Degrees of Freedom robots. She focusesher attention on the resolution of the Navigation AmongMovable Obstacles problem.
Enrico Pagello is a Full Professor of Computer Science andRobotics at the Dept. of Information Eng. Of the Universityof Padua. From ’73 till ’83, he has been a ResearchAssociateof the National Research Council of Italy, where now heis a part-time Senior Scientist. During 77–78, he was aVisiting Scholar at the Lab. of A.I. of Stanford Univ. He isa Fellow of the Univ. of Tokyo. He was a President of theIntelligent Autonomous Systems Society, and was a Vice-President of the RoboCup Int. Federation. He was a Co-Guest Editor of two Special Issues (SI) of the Robotics andAutonomous Systems Journal, one SI of IEEE/Transaction
on Robotics and Automation, and one SI of the Int. J. of Humanoids Robotics. Hisresearch interests are on applying Artificial Intelligence to Robotics.
Emanuele Menegatti received his Ph.D. in Degree inComputer Engineering in 2003 at the Dept. of InformationEng. of the University of Padua. In 2005, he became anAssistant Professor at University of Padua. Since, 2010he has been an Associate Professor of the Faculty ofEngineering of the University of Padua. In 2002 and 2004he was visiting scientist at the University of Osaka (JP) andat Georgia Tech (USA). He was a Co-Guest Editor for threespecial issues of the Robotics and Autonomous SystemsJournal. His research interests are in the field of RobotVision and 3D perception systems. He is author of more
than 130 scientific publications and chair of workshops and conferences.
