Department Talks
  • Todor Stoyanov and Robert Krug
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

In this talk we will give an overview of research efforts within autonomous manipulation at the AASS Research Center, Örebro University, Sweden. We intend to give a holistic view on the historically separated subjects of robot motion planning and control. In particular, viewing motion behavior generation as an optimal control problem allows for a unified formulation that is uncluttered by a-priori domain assumptions and simplified solution strategies. Furthermore, We will also discuss the problems of workspace modeling and perception and how to integrate them in the overarching problem of autonomous manipulation.

Organizers: Ludovic Righetti


  • Matteo Turchetta
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

In classical reinforcement learning agents accept arbitrary short term loss for long term gain when exploring their environment. This is infeasible for safety critical applications such as robotics, where even a single unsafe action may cause system failure or harm the environment. In this work, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an a priori unknown safety constraint that depends on states and actions and satisfies certain regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm, SAFEMDP, for this task and prove that it completely explores the safely reachable part of the MDP without violating the safety constraint. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.

Organizers: Sebastian Trimpe


Power meets Computation

Talk
  • 13 January 2017 • 11:00 12:30
  • Dr. Thomas Besselmann
  • AMD seminar room (PES 15)

This is the story of the novel model predictive control (MPC) solution for ABB’s largest drive, the Megadrive LCI. LCI stands for load commutated inverter, a type of current source converter which powers large machineries in many industries such as marine, mining or oil & gas. Starting from a small software project at ABB Corporate Research, this novel control solution turned out to become the first time ever MPC was employed in a 48 MW commercial drive. Subsequently it was commissioned at Kollsnes, a key facility of the natural gas delivery chain, in order to increase the plant’s availability. In this presentation I will talk about the magic behind this success story, the so-called Embedded MPC algorithms, and my objective will be to demonstrate the possibilities when power meets computation.

Organizers: Sebastian Trimpe


Intelligent control of uncertain underactuated mechanical systems

Talk
  • 01 December 2016 • 11:00 - 01 November 2016 • 12:00
  • Wallace M. Bessa
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

Underactuated mechanical systems (UMS) play an essential role in several branches of industrial activity and their application scope ranges from robotic manipulators and overhead cranes to aerospace vehicles and watercrafts. Despite this broad spectrum of applications, the problem of designing accurate controllers for underactuated systems is, however, much more tricky than for fully actuated ones. Moreover, the dynamic behavior of an UMS is frequently uncertain and highly nonlinear, which in fact makes the design of control schemes for such systems a challenge for conventional and well established methods. In this talk, it will be shown that intelligent algorithms, such as fuzzy logic and artificial neural networks, could be combined with nonlinear control techniques (feedback linearization or sliding modes) in order to improve both set-point regulation and trajectory tracking of uncertain underactuated mechanical systems.

Organizers: Sebastian Trimpe


Optical Robot Skin and Whole Body Vision

Talk
  • 19 October 2016 • 14:00 15:00
  • Chris Atkeson and Akihiko Yamaguchi
  • Max Planck House, Lecture Hall

Chris Atkeson will talk about the motivation for optical robot skin and whole-body vision. Akihiko Yamaguchi will talk about a first application, FingerVision.

Organizers: Ludovic Righetti


  • Jose R. Medina
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

Control under uncertainty is an omnipresent problem in robotics that typically arises when robots must cope with unknown environments/tasks. Robot control typically ignores uncertainty by considering only the expected outcomes of the robot’s internal model. Interestingly, neuroscientist have shown that humans adapt their decisions depending on the level of uncertainty which is not reflected in the expected values, but in higher order statistics. In this talk I will first present an approach to systematically address this problem in the context of stochastic optimal control. I will then give an example of how the robot’s internal model structure defines the level uncertainty and its distribution. Finally, experiments in a physical human-robot interaction setting will illustrate the capabilities of this approach.

Organizers: Ludovic Righetti


  • Stéphane Caron
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

Humanoid locomotion on horizontal floors was solved by closing the feedback loop on the Zero-tiling Moment Point (ZMP), a measurable dynamic point that needs to stay inside the foot contact area to prevent the robot from falling (contact stability criterion). However, this criterion does not apply to general multi-contact settings, the "new frontier" in humanoid locomotion. In this talk, we will see how the ideas of ZMP and support area can be generalized and applied to multi-contact locomotion. First, we will show how support areas can be calculated in any virtual plane, allowing one to apply classical schemes even when contacts are not coplanar. Yet, these schemes constraint the center-of-mass (COM) to planar motions. We overcome this limitation by extending the calculation of the contact-stability criterion from a support area to a support cone of 3D COM accelerations. We use this new criterion to implement a multi-contact walking pattern generator based on predictive control of COM accelerations, which we will demonstrate in real-time simulations during the presentation.

Organizers: Ludovic Righetti


  • Christian Ebenbauer
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

In many control applications it is the goal to operate a dynamical system in an optimal way with respect to a certain performance criterion. In a combustion engine, for example, the goal could be to control the engine such that the emissions are minimized. Due to the complexity of an engine, the desired operating point is unknown or may even change over time so that it cannot be determined a priori. Extremum seeking control is a learning-control methodology to solve such kind of control problems. It is a model-free method that optimizes the steady-state behavior of a dynamical system. Since it can be implemented with very limited resources, it has found several applications in industry. In this talk we give an introduction to extremum seeking theory based on a recently developed framework which relies on tools from geometric control. Furthermore, we discuss how this framework can be utilized to solve distributed optimization and coordination problems in multi-agent systems.

Organizers: Sebastian Trimpe


Safe Learning Control for Mobile Robots

IS Colloquium
  • 25 April 2016 • 11:15 12:15
  • Angela Schoellig
  • Max Planck Haus Lecture Hall

In the last decade, there has been a major shift in the perception, use and predicted applications of robots. In contrast to their early industrial counterparts, robots are envisioned to operate in increasingly complex and uncertain environments, alongside humans, and over long periods of time. In my talk, I will argue that machine learning is indispensable in order for this new generation of robots to achieve high performance. Based on various examples (and videos) ranging from aerial-vehicle dancing to ground-vehicle racing, I will demonstrate the effect of robot learning, and highlight how our learning algorithms intertwine model-based control with machine learning. In particular, I will focus on our latest work that provides guarantees during learning (for example, safety and robustness guarantees) by combining traditional controls methods (nonlinear, robust and model predictive control) with Gaussian process regression.

Organizers: Sebastian Trimpe


  • Felix Berkenkamp
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

Bayesian optimization is a powerful tool that has been successfully used to automatically optimize the parameters of a fixed control policy. It has many desirable properties, such as data-efficiently and being able to handle noisy measurements. However, standard Bayesian optimization does not consider any constraints imposed by the real system, which limits its applications to highly controlled environments. In this talk, I will introduce an extension of this framework, which additionally considers multiple safety constraints during the optimization process. This method enables safe parameter optimization by only evaluating parameters that fulfill all safety constraints with high probability. I will show several experiments on a quadrotor vehicle which demonstrate the method. Lastly, I will briefly talk about how the ideas behind safe Bayesian optimization can be used to safely explore unknown environments (MDPs).

Organizers: Sebastian Trimpe