April 17th, 2012

Learning acrobatic maneuvers for quadrocopters - article


Adaptive fast open-loop maneuvers for quadrocopters
Sergei Lupashin, Raffaello D'Andrea

Have you ever seen those videos of quadrocopters performing acrobatic maneuvers?

The latest paper on the Autonomous Robots website presents a simple method to make your robot achieve adaptive fast open-loop maneuvers, whether it’s performing multiple flips or fast translation motions. The method is thought to be straightforward to implement and understand, and general enough that it could be applied to problems outside of aerial acrobatics.

Before the experiment, an engineer with knowledge of the problem defines a maneuver as an initial state, a desired final state, and a parameterized control function responsible for producing the maneuver. A model of the robot motion is used to initialize the parameters of this control function. Because models are never perfect, the parameters then need to be refined during experiments. The error between the robot’s desired state and its achieved state after each maneuver is used to iteratively correct parameter values. More details can be found in the figure below or in the paper.

Method to achieve adaptive fast open-loop maneuver. p represents the parameters to be adapted, C is a first-order correction matrix, γ is a correction step size, and e is a vector of error measurements. (1) The user defines a motion in terms of initial and desired final states and a parameterized input function. (2) A first-principles continuous-time model is used to find nominal parameters p0 and C. (3) The motion is performed on the physical vehicle, (4) the error is measured and (5) a correction is applied to the parameters. The process is then repeated.

Experiments were performed in the ETH Flying Machine Arena which is equipped with an 8-camera motion capture system providing robot position and rotation measurements used for parametric learning.

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January 17th, 2012

iCub drums and crawls using bio-inspired control - article


Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives
Sarah Degallier, Ludovic Righetti, Sebastien Gay, Auke Ijspeert

Ever see a lizard effortlessly run up a wall?

Like most vertebrates, lizards are able to quickly adapt to new environments in a robust way thanks to a special type of movement generator. The idea is that a high-level planner (the brain) is responsible for determining the key characteristics of a movement such as the position that needs to be reached by a limb or the amplitude and frequency with which the limbs should perform rhythmic motions. These high-level commands then serve as an input to motion primitives responsible for activating muscles in the correct sequence. Motion primitives are typically organized at the spinal level through neural networks called central pattern generators (CPGs).

This control architecture has many advantages for robotics. First, once the motion primitives are designed, only high-level commands are required to control the entire motion of the robot. Therefor, instead of planning the positions of all joints, the motion planner only needs to issue high-level goals such as “reach there” or “move your arm rhythmically with this amplitude and this frequency”. This greatly reduces the complexity of planning motions for robots with many degrees of freedom. Furthermore, CPGs are very fast, have low computational cost and can be modulated by sensory feedback in order to obtain adaptive behaviors.

Using this control architecture, Degallier et al. were able to turn the iCub humanoid seen in the video below into an on-demand drummer. Random users at a robotics conference were able to change on-line a score that the iCub was playing or test how well it could adapt when its drums were moved. To show the generality of their approach, they then applied the same architecture to make the iCub crawl and reach for objects. Although one behaviour was rhythmic (crawling) and the other discrete (reaching), the robot was easily able to switch between the two.

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January 2nd, 2012

Cooperative modular satellites - article


Cooperative control of modular space robots
Chiara Toglia, Fred Kennedy, Steven Dubowsky

In Modular Space Robotics, modules self-assemble while in orbit to create larger satellites for specific missions. Modular satellites have the potential to reduce mission costs (small satellites are cheaper to launch), increase reliability, and enable on-orbit repair and refueling. Each of the modules has its load of sensors, fuel and attitude control actuators (thrusters). Assembled modules therefore have redundant sensor and actuation capabilities. By fusing sensor data, the modular satellites can follow its trajectory more precisely and smart thruster activation can help save fuel.

The challenge is to figure out how to control such a self-assembled robot to minimize fuel consumption while balancing fuel distribution and improve trajectory following. To this end, Toglia et al. propose a cooperative controller where one of the modules, with information about the configuration of all other modules, is responsible for computing an optimal control schema. An extended Kalman-Bucy Filter is used to implement sensor fusion.

The cooperative controller was compared to an independent controller where each module attempts to follow its own trajectory while minimizing its own fuel usage and trajectory errors. Results from simulation and reality show that the cooperative controller can save significant amounts of fuel, up to 43% in one experiment, while making the trajectories more precise.

Experiments in reality were performed with two satellites using the MIT Field and Space Robotics Laboratory Free-Flying Space Robot Test Bed shown below.

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December 1st, 2011

Teaching motion primitives - article


Incremental kinesthetic teaching of motion primitives using the motion refinement tube
Dongheui Lee, Christian Ott

Finding ways to easily teach service robots new motions will be key to their integration in our everyday environments. Ideally, teaching a robot should be no different than teaching a human.

For example, to teach someone a new dance, you might first show them the basic steps. You will most likely mention motion primitives, such as “right foot forward” and not the actual position of all your body joints. The apprentice dancer will then try to imitate your steps. To refine dance moves, the teacher can physically correct the motion by pushing the elbow higher, straightening the back or guiding the steps. However, if the student has been taught to move forward with its right foot, and the teacher pushes in the opposite direction, the dancer will most likely freeze. This is due to the fact that refinements should fit within a certain region around the movement that the person expects (refinement tube). Over time, the dancer iteratively improves its movements, forgetting older clumsy moves along the way.

Following this exact idea, Lee et al. have been teaching motion primitives to the humanoid upper-body robot “Justin”. Experiments use the 19 joints of the arms (2 times 7 DOF), torso (3 DOF), and head (2 DOF). The framework shown in the schematic below, uses imitation learning followed by iterative kinesthetic motion refinements (physically guided corrections) within a refinement tube. Motion primitives are represented as a hidden Markov Model.

The authors hope that in the future, these algorithms can contribute to making humanoid robots, which are capable of autonomous long-term learning and adaptation.

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November 30th, 2011

Robotic Musicianship - article


Interactive improvisation with a robotic marimba player
Guy Hoffman, Gil Weinberg

Shimon is an interactive robotic marimba player that can improvise both music and choreography in real-time to the melody of a human pianist.

Playing an instrument does not make you a musician. To become a musician you need to listen, analyze, improvise, and interact through the sound you produce and your body language.

With this in mind, Hoffman et al. explore robotic musicianship. Unlike robots that simply perform a sequence of notes, Shimon’s performances are composed of a sequence of gestures that may or may not produce sound. Using gestures as the building blocks of musical expression is particularly appropriate for robotic musicianship, and nicely fits with our embodied view of human-robot interaction.

The robot is able to improvise by following basic aspects of standard Jazz joint improvisation and can anticipate gestures to easily synchronize with duet partners. Building on this, the human and robot could perform three types of interactions. In the first interaction, the robot and human played two distinct musical phrases, where the second phrase is a commentary on the first phrase. The second interaction was centered around the choreographic aspect of movement with the notes appearing as a “side-effect” of the performance. The third interaction was a rhythmic phrase-matching improvisation.

Using this improvisation system, the pair performed full-length performances of nearly 7 minutes in front of live public audiences and more than 70’000 online viewers.

After the live performances, additional experiments were conducted to investigate the importance of physical embodiment and visual contact in Robotic Musicianship. Results show that synchronization between the robot and musician can be aided by visual contact when the tempo is uncertain and slow. In addition, the audience perceives Shimon as playing better, more like a human, as more responsive, and even more inspired when compared to a “computer musician”. Shimon was also rated as better synchronized, more coherent, communicating, and coordinated; and the human as more inspired and more responsive.

In the future, Hoffman et al. hope to further explore robot musicianship by giving Shimon a socially expressive robot head, vision and new gestures.

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November 12th, 2011

Centipede microrobots - article


Myriapod-like ambulation of a segmented microrobot
Katie L. Hoffman, Robert J. Wood

Improvements in microfabricaton techniques and an increased understanding of insect locomotion has led to the development of impressive centimeter-sized legged robots. The cockroach-like robots seen in the videos below typically have rigid bodies and a set number of legs.


Following an alternative approach, Hoffman et al. developed a myriapod inspired robot with a flexible backbone and modular number of legs. The added flexibility and potentially large leg-count is expected to increase speed, robustness and stability while helping the robot adapt to difficult terrain or climb.

Because controlling many-legged robots with a flexible backbone is challenging, a dynamic model of the system was designed to predict the behavior of the myriapod and optimize its body parameters.

The resulting six-legged version of the robot seen below weighs 750 mg and is 3.5 by 3.5 cm. The fabrication of such a small robot is done using a Smart Composite Microstructures process that involves sandwiching a flexible material between two layers of rigid material such as carbon fiber. Flexures are created by making precise incisions in the carbon fiber, thereby revealing the flexible material which is then free to bend. Flexures can be solidified at any angle using glue or can be left flexible. What started out as a 2D structure is therefore folded into a 3D mechanical structure. Actuation is added by layering piezoelectric material, carbon fiber and glass fiber.

Results show the robot walking forward at a pace of 1 body length in 10 seconds, with a step size between 0.75 and 1 mm. The step size is dependent on the gait and is expected to vary with different body undulations.

The following video shows the latest version of the robot with 20 legs and insect-like body undulations.


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November 9th, 2011

Underwater ROV keeps its eye on the target - article


Towards semi-autonomous operation of under-actuated underwater vehicles: sensor fusion, on-line identification and visual servo control
George C. Karras, Savvas G. Loizou, Kostas J. Kyriakopoulos

Underwater robots can be used to inspect water-bound structures such as ship hulls. Remote-operating these vehicles can be tricky if they are under-actuated, meaning you can’t fully control their motion. For example, you might command the robot to go forward but uncontrolled sway could result in the robot moving sideways. This can be challenging if you’re trying to inspect an object using a camera, and the object keeps slipping out of your field of view.

To solve this problem, Karras et al. propose a semi-autonomous control scheme that ensures the robot doesn’t lose sight of the inspection target. The control fuses information (using an asynchronous Modified Dual Unscented Kalman Filter) from sensors on the robot to estimate its position and attitude and correct its trajectory when needed.

Experiments were conducted in a test tank using an under-actuated underwater robot that uses only three thrusters. Information for sensor fusion is provided by an Inertial Measurement Unit (IMU), a camera, and two laser pointers which are parallel to the camera axis. By monitoring where the lasers point using the camera, the robot can figure out how it is moving with respect to the inspection target.

Results show the feasibility and applicability of the semi-autonomous control scheme. In the future, the authors hope to extend their approach to more difficult tasks such as inspecting fish farm nets.

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November 8th, 2011

Learning hand motions from humans - article


Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures
Jan Rosell, Raúl Suárez, Carlos Rosales, Alexander Pérez

Although human hands have lots of degrees of freedom, we typically don’t use most configurations. For example, we usually don’t move the last two joints of our fingers independently. Now let’s look at the anthropomorphic robot hand below. Like the human hand, it has lots of degrees of freedom and planning a motion would typically take a lot of time if we consider all possibilities. To solve this problem, Rosell et al. propose to look at what motions humans do, and use the information to limit the motions the robot hand should be doing.

Industrial robot Stäubli TX 90 with the mechanical hand Schunk Anthropomorphic Hand.

To learn about human hand motion they fitted a human with a sensorized glove and recorded its movements. The human movements were then translated into robot coordinates. Using a technique called Principal Component Analysis, the robot is able to extract the most important motions that humans do. By combining these principal motions with a planner to make sure the arm and hand don’t collide with the environment or their own parts, the robot is able to perform human-like motion using little computation.

The approach was validated in simulation and using a four finger anthropomorphic mechanical hand (17 joints with 13 in- dependent degrees of freedom) assembled on an industrial robot (6 independent degrees of freedom).

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October 12th, 2011

From iCub to artist - article


Teaching a humanoid robot to draw Shapes
Vishwanathan Mohan, Pietro Morasso, Jacopo Zenzeri, Giorgio Metta, V. Srinivasa Chakravarthy, Giulio Sandini

Learning how to perceive shapes and act on them is what allows us to interact with our world. Whether it’s grasping for an object, drawing or dancing.

With this in mind, Mohan et al. have been teaching the iCub, a child-sized humanoid, to draw shapes. Starting from simple shapes like ‘I’ and ‘U’ the robot goes on to writing its full name and finally drawing a portrait of Gandhi.

To do this, the iCub observes a human teacher drawing the shape before trying to reproduce it. The task requires a host of skills such as vision processing, imitating the teacher’s motions, practicing the drawings, exploring new actions, trajectory formation and inverse kinematics, and finally generalizing lessons learned to new tasks.

For a complete explanation of how the system works, have a look at the excellent presentation below narrated by the author of the paper.

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September 26th, 2011

Compliant actuator for 1DOF hopper - article


MACCEPA 2.0: compliant actuator used for energy efficient hopping robot Chobino1D
Bram Vanderborght, Nikos G. Tsagarakis, Ronald Van Ham, Ivar Thorson, Darwin G. Caldwell

For a long time, robots were seen as rigid machines driven by sturdy motors. This raised worries concerning the safety of people interacting with them. One option to make robots safer is to equip them with compliant actuators that can adapt to external forces, such as a human getting in the way. Note that most natural systems also rely on compliant actuators such as muscles that can store energy, thereby making them more efficient for tasks such as running or hopping.

Building on the potential of safe and energy efficient actuators, Vanderborght et al. propose a new type of actuator called MACCEPA 2.0 (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator). As seen in the figure below, when the position of the profile disk (heart shape) is changed by a servomotor or the joint is bent, this causes the tendon that is guided over the profile to pull on the spring. To counteract the pulling force, a torque will be generated that depends on the shape of the profile. To change the compliance of the actuator, simply replace the profile by another shape. Similar to what happens in human legs, the stiffness of the actuator increases with joint flexion.

Working principle of the MACCEPA 2.0. Top: Bent position (generating torque). Middle: At equilibrium position (not generating torque). Bottom: Preloaded spring caused by rotating profile.

The actuator was demonstrated on the 1DOF hopping robot Chobino1D shown below. The spring is preloaded by turning the profile using a servomotor before releasing the tension for the jump. Using MACCEPA 2.0, the robot was able to jump much higher than a robot with a stiff actuator.

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