Department Talks
  • Alexander Sprowitz
  • TTR, AMD Seminar Room (first floor)

The current performance gap between legged animals and legged robots is large. Animals can reach high locomotion speed in complex terrain, or run at a low cost of transport. They are able to rapidly sense their environment, process sensor data, learn and plan locomotion strategies, and execute feedforward and feedback controlled locomotion patterns fluently on the fly. Animals use hardware that has, compared to the latest man-made actuators, electronics, and processors, relatively low bandwidth, medium power density, and low speed. The most common approach to legged robot locomotion is still assuming rigid linkage hardware, high torque actuators, and model based control algorithms with high bandwidth and high gain feedback mechanisms. State of the art robotic demonstrations such as the 2015 DARPA challenge showed that seemingly trivial locomotion tasks such as level walking, or walking over soft sand still stops most of our biped and quadruped robots. This talk focuses on an alternative class of legged robots and control algorithms designed and implemented on several quadruped and biped platforms, for a new generation of legged robotic systems. Biomechanical blueprints inspired by nature, and mechanisms from locomotion neurocontrol were designed, tested, and can be compared to their biological counterparts. We focus on hardware and controllers that allow comparably cheap robotics, in terms of computation, control, and mechanical complexity. Our goal are highly dynamic, robust legged systems with low weight and inertia, relatively low mechanical complexity and cost of transport, and little computational demands for standard locomotion tasks. Ideally, such system can also be used as testing platforms to explain not yet understood biomechanical and neurocontrol aspects of animals.

Organizers: Ludovic Righetti


Making Robots Learn

IS Colloquium
  • 13 November 2015 • 11:30 12:30
  • Prof. Pieter Abbeel
  • Max Planck House Tübingen, Lecture Hall

Programming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what often ends up being time-consuming task specific programming. In this talk I will describe the ideas behind two promising types of robot learning: First I will discuss apprenticeship learning, in which robots learn from human demonstrations, and which has enabled autonomous helicopter aerobatics, knot tying, basic suturing, and cloth manipulation. Then I will discuss deep reinforcement learning, in which robots learn through their own trial and error, and which has enabled learning locomotion as well as a range of assembly and manipulation tasks.

Organizers: Stefan Schaal


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

Unknown information required to plan grasps such as object shape and pose needs to be extracted from the environment through sensors. However, sensory measurements are noisy and associated with a degree of uncertainty. Furthermore, object parameters relevant to grasp planning may not be accurately estimated, e.g., friction and mass. In real-world settings, these issues can lead to grasp failures with serious consequences. I will talk about learning approaches using real sensory data, e.g., visual and tactile, to assess grasp success (discriminative and generative) that can be used to trigger plan corrections. I will also present a probabilistic approach for learning object models based on visual and tactile data through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape.

Organizers: Jeannette Bohg


  • Anna Belardinelli
  • Max Planck House Lecture Hall

Our eyes typically anticipate the next action module in a sequence, by targeting the relevant object for the following step. Yet, how the final goal, or the way we intend to achieve it, is reflected in the early visual exploration of each object has been less investigated. In a series of experiments we considered how scan paths on real-world objects would be affected by different factors such as task, object orientation, familiarity, or low-level saliency, hence revealing which components can account for fixation target selection during eye-hand coordination. In each experiment, the fixation distribution differed significantly depending on the final task (e.g. lifting vs. opening). Already from the second fixation prior to reaching the object the eyes targeted the task-relevant regions and these significantly correlated with salient features like oriented edges. Familiarity had a significant effect when different tools were used as stimuli, with more fixations concentrating on the active end of unfamiliar tools. Object orientation (upright or inverse) and anticipation of the final comfort state determined the height of the fixations on the objects. Scan paths dynamics, thus, reveal how action is planned, offering indirect insight in the structuring of complex behaviour and the understanding of how task and affordance perception relates to motor control.

Organizers: Jeannette Bohg


Autonomous Systems At Moog

Talk
  • 06 July 2015 • 14:00 15:00
  • Gonzalo Rey
  • AMD Seminar Room

The talk will briefly introduce Moog Inc. It will then describe Moog's view of its value proposition to robotics and autonomous systems. If robots and autonomous system are to achieve their enormous potential to positively impact the world economy, the technology has to achieve equivalent the levels of robustness, availability, reliability and safety that are expected from current solutions. The commercial aircraft industry has seen an order of magnitude increase in machine complexity in the last fifty years in order to reach the highest ever levels of cost per seat-mile and safety in its history. Today one can travel cheaper and safer than ever. Moog believes that there are opportunities to apply the methodologies and principles that enabled the lowest ever costs while at the same time managing the highest ever complexity and safety levels for aircraft to robotics and autonomous systems. The talk will briefly describe the type of approaches used in aircraft to achieve such low levels of failures that are hard to comprehend (or believe for those not familiar with the engineering approach), while at the same time, relying on low cost commercial off the shelf components in electronics, materials and manufacturing processes. Next the talk will move onto a couple of active research projects Moog is engaged in with ETHZ and IIT. Finally, it will give an overview of an emerging research effort in certification of advanced (robot) control laws.

Organizers: Ludovic Righetti


  • Andre Seyfarth
  • MRZ Seminar Room

In this presentation a series of conceptual models for describing human and animal locomotion will be presented ranging from standing to walking and running. By subsequently increasing the complexity of the models we show that basic properties of the underlying spring-mass model can be inherited by the more detailed models. Model extensions include the consideration of a rigid trunk (instead of a point mass), non-elastic leg properties (instead of a mass-less leg spring), additional legs (two and four legs), leg masses, leg segments (e.g. a compliantly attached foot) and energy management protocols. Furthermore we propose a methodology to evaluate and refine conceptual models based on the test trilogy. This approach consists of a simulation test, a hardware test and a behavioral comparison of biological experiments with model predictions and hardware models.


  • Andre Seyfarth
  • MRZ Seminar room

In this presentation a series of conceptual models for describing human and animal locomotion will be presented ranging from standing to walking and running. By subsequently increasing the complexity of the models we show that basic properties of the underlying spring-mass model can be inherited by the more detailed models. Model extensions include the consideration of a rigid trunk (instead of a point mass), non-elastic leg properties (instead of a mass-less leg spring), additional legs (two and four legs), leg masses, leg segments (e.g. a compliantly attached foot) and energy management protocols. Furthermore we propose a methodology to evaluate and refine conceptual models based on the test trilogy. This approach consists of a simulation test, a hardware test and a behavioral comparison of biological experiments with model predictions and hardware models.

Organizers: Ludovic Righetti


  • Auke Ijspeert
  • Max Planck Lecture Hall

Organizers: Ludovic Righetti


Quadrupedal locomotion: a planning & control framework for HyQ

Talk
  • 09 March 2015 • 11:00 am 12:00 am
  • Ioannis Havoutis
  • AMD seminar room (TTR building first floor)

It is a great pleasure to invite you to the talk of Ioannis Havoutis (cf. info below) on Monday March 9th at 11h in the AMD seminar room (TTR building, first floor). have a nice week-end, ludovic Quadrupedal animals move with skill, grace and agility. Quadrupedal robots have made tremendous progress in the last few years. In this talk I will give an overview of our work with the Hydraulic Quadruped -HyQ- and present our latest framework for perception, planning and control of quadrupedal locomotion in challenging environments. In addition, I will give a short preview of our work on optimization of dynamic motions, and our future goals.

Organizers: Ludovic Righetti


Introduction to the Scenario Approach

IS Colloquium
  • 19 January 2015 • 11:15 12:15
  • Marco Claudio Campi
  • MPH Lecture Hall, Tübingen

The scenario approach is a broad methodology to deal with decision-making in an uncertain environment. By resorting to observations, or by sampling uncertainty from a given model, one obtains an optimization problem (the scenario problem), whose solution bears precise probabilistic guarantees in relation to new, unseen, situations. The scenario approach opens up new avenues to address data-based problems in learning, identification, finance, and other fields.

Organizers: Sebastian Trimpe