Fingertip skin friction plays a critical role during object manipulation. We will describe a simple and reliable method to estimate the fingertip static coefficient of friction (CF) continuously and quickly during object manipulation, and we will describe a global expression of the CF as a function of the normal force and fingertip moisture. Then we will show how skin hydration modifies the skin deformation dynamics during grip-like contacts. Certain motor behaviours observed during object manipulation could be explained by the effects of skin hydration. Then the biomechanics of the partial slip phenomenon will be described, and we will examine how this partial slip phenomenon is related to the subjective perception of fingertip slip.
A new concept of using permanent magnet systems for guiding superparamagnetic nano-particles (SPP) on arbitrary trajectories over a large volume is presented. The same instrument can also be used for magnetic resonance imaging (MRI) using the inherent contrast of the SPP . The basic idea is to use one magnet system, which provides a strong, homogeneous, dipolar magnetic field to magnetize and orient the particles, and a second constantly graded, quadrupolar field, superimposed on the first, to generate a force on the oriented particles. As a result, particles are guided with constant force and in a single direction over the entire volume. Prototypes of various sizes were constructed to demonstrate the principle in two dimensions on several nanoparticles, which were moved along a rough square by manual adjustment of the force angle . Surprisingly even SPP with sizes < 100 nm could be moved with speeds exceeding 10 mm/s due to reversible agglomeration, for which a first hydrodynamic model is presented. Furthermore, a more advanced system with two quadrupoles is presented which allows canceling the force, hence stopping the SPP and moving them around sharp edges. Additionally, this system also allows for MRI and some first experiments are presented. Recently this concept was combined with liquid crystalline elastomers with incorporated SPP to create “micro-robots” whose coarse maneuvers are performed by a MagGuider-system while there microscopic actuation is controlled either by light or temperature . 1. O. Baun, PB, JMMM 439 (2017) 294-304. doi: 10.1016/j.jmmm.2017.05.001 2. D. Ditter, PB et al. Adv. Functional Mater. 1902454 (2019) doi: 10.1002/adfm.201902454
Organizers: Metin Sitti
Future cities and infrastructure systems will evolve into complex conglomerates where autonomous aerial, aquatic and ground-based robots will coexist with people and cooperate in symbiosis. To create this human-robot ecosystem, robots will need to respond more flexibly, robustly and efficiently than they do today. They will need to be designed with the ability to move across terrain boundaries and physically interact with infrastructure elements to perform sensing and intervention tasks. Taking inspiration from nature, aerial robotic systems can integrate multi-functional morphology, new materials, energy-efficient locomotion principles and advanced perception abilities that will allow them to successfully operate and cooperate in complex and dynamic environments. This talk will describe the scientific fundamentals, design principles and technologies for the development of biologically inspired flying robots with adaptive morphology that can perform monitoring and manufacturing tasks for future infrastructure and building systems. Examples will include flying robots with perching capabilities and origami-based landing systems, drones for aerial construction and repair, and combustion-based jet thrusters for aerial-aquatic vehicles.
Organizers: Metin Sitti
This talk addresses the task of segmenting moving objects in unconstrained videos. We introduce a novel two-stream neural network with an explicit memory module to achieve this. The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. Given video frames as input, our approach first assigns each pixel an object or background label obtained with an encoder-decoder network that takes as input optical flow and is trained on synthetic data. Next, a “visual memory” specific to the video is acquired automatically without any manually-annotated frames. The visual memory is implemented with convolutional gated recurrent units, which allows to propagate spatial information over time. We evaluate our method extensively on two benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show state-of-the-art results. This is joint work with K. Alahari and P. Tokmakov.
Organizers: Osman Ulusoy
Many of the existing Robotics & Automation (R&A) technologies are at a sufficient level of maturity and are widely accepted by the academic (and to a lesser extent by the industrial) community after having undergone the scientific rigor and peer reviews that accompany such works. I believe that most of the past and current research and development efforts in robotics and automation have been squarely aimed at increasing the Standard of Living (SoL) in developed economies where housing, running water, transportation, schools, access to healthcare, to name a few, are taken for granted. Humanitarian R&A, on the other hand, can be taken to mean technologies that can make a fundamental difference in people’s lives by alleviating their suffering in times of need, such as during natural or man-made disasters or in pockets of the population where the most basic needs of humanity are not met, thus improving their Quality of Life (QoL) and not just SoL. My current work focuses on the applied use of robotics and automation technologies for the benefit of under-served and under-developed communities by working closely with them to develop solutions that showcase the effectiveness of R&A solutions in domains that strike a chord with the beneficiaries. This is made possible by bringing together researchers, practitioners from industry, academia, local governments, and various entities such as the IEEE Robotics Automation Society’s Special Interest Group on Humanitarian Technology (RAS-SIGHT), NGOs, and NPOs across the globe. I will share some of my efforts and thoughts on challenges that need to be taken into consideration including sustainability of developed solutions. I will also outline my recent efforts in the technology and public policy domains with emphasis on socio-economic, cultural, privacy, and security issues in developing and developed economies.
Organizers: Ludovic Righetti
I'll present my master thesis "Biquadratic Forms and Semi-Definite Relaxations". It is about biquadratic optimization programs (which are NP-hard generally) and examines a condition under which there exists an algorithm that finds a solution to every instance of the problem in polynomial time. I'll present a counterexample for which this is not possible generally and face the question of what happens if further knowledge about the variables over which we optimise is applied.
Organizers: Fatma Güney
A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. This talk is about optimization problems whose feasible solutions define decompositions of a graph. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows applications of this problem and proposed generalizations to diverse image analysis tasks. It sketches algorithms for finding feasible solutions for large instances in practice, solutions that are often superior in the metrics of application-specific benchmarks. It also sketches algorithms for finding lower bounds and points to new findings and open problems of polyhedral geometry in this context.
Organizers: Christoph Lassner
Colloquium on haptics: Two guests of the department "Haptic Intelligence" (Dept. Kuchenbecker), will each give a short talk this Friday (May 5) in Tübingen. The talks will be broadcasted to Stuttgart, room 2 P4.
Estimating human pose, shape, and motion from images and video are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL: a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.
Organizers: Dimitrios Tzionas
Human-centric robotic applications often require the robots to learn new skills by interacting with the end-users. From a machine learning perspective, the challenge is to acquire skills from only few interactions, with strong generalization demands. It requires: 1) the development of intuitive active learning interfaces to acquire meaningful demonstrations; 2) the development of models that can exploit the structure and geometry of the acquired data in an efficient way; 3) the development of adaptive control techniques that can exploit the learned task variations and coordination patterns. The developed models often need to serve several purposes (recognition, prediction, online synthesis), and be compatible with different learning strategies (imitation, emulation, exploration). For the reproduction of skills, these models need to be enriched with force and impedance information to enable human-robot collaboration and to generate safe and natural movements. I will present an approach combining model predictive control and statistical learning of movement primitives in multiple coordinate systems. The proposed approach will be illustrated in various applications, with robots either close to us (robot for dressing assistance), part of us (prosthetic hand with EMG and tactile sensing), or far from us (teleoperation of bimanual robot in deep water).
Organizers: Ludovic Righetti
This talk will survey recent work to achieve multi-contact locomotion control of humanoid and legged robots. I will start by presenting some results on robust optimization-based control. We exploited robust optimization techniques, either stochastic or worst-case, to improve the robustness of Task-Space Inverse Dynamics (TSID), a well-known control framework for legged robots. We modeled uncertainties in the joint torques, and we immunized the constraints of the system to any of the realizations of these uncertainties. We also applied the same methodology to ensure the balance of the robot despite bounded errors in the its inertial parameters. Extensive simulations in a realistic environment show that the proposed robust controllers greatly outperform the classic one. Then I will present preliminary results on a new capturability criterion for legged robots in multi-contact. "N-step capturability" is the ability of a system to come to a stop by taking N or fewer steps. Simplified models to compute N-step capturability already exist and are widely used, but they are limited to locomotion on flat terrains. We propose a new efficient algorithm to compute 0-step capturability for a robot in arbitrary contact scenarios. Finally, I will present our recent efforts to transfer the above-mentioned techniques to the real humanoid robot HRP-2, on which we recently implemented joint torque control.
Organizers: Ludovic Righetti
The retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. I will review our recent contributions in this area, showing how analyzing multimodal datasets from electron microscopy and functional imaging can yield insights into the cellular organization of retinal circuits.
Organizers: Philipp Hennig
From gait, dance to martial art, human movements provide rich, complex yet coherent spatiotemporal patterns reflecting characteristics of a group or an individual. We develop computer algorithms to automatically learn such quality discriminative features from multimodal data. In this talk, I present a trilogy on learning from human movements: (1) Gait analysis from video data: based on frieze patterns (7 frieze groups), a video sequence of silhouettes is mapped into a pair of spatiotemporal patterns that are near-periodic along the time axis. A group theoretical analysis of periodic patterns allows us to determine the dynamic time warping and affine scaling that aligns two gait sequences from similar viewpoints for human identification. (2) Dance analysis and synthesis (mocap, music, ratings from Mechanical Turks): we explore the complex relationship between perceived dance quality/dancer's gender and dance movements respectively. As a feasibility study, we construct a computational framework for an analysis-synthesis-feedback loop using a novel multimedia dance-texture representation for joint angular displacement, velocity and acceleration. Furthermore, we integrate crowd sourcing, music and motion-capture data, and machine learning-based methods for dance segmentation, analysis and synthesis of new dancers. A quantitative validation of this framework on a motion-capture dataset of 172 dancers evaluated by more than 400 independent on-line raters demonstrates significant correlation between human perception and the algorithmically intended dance quality or gender of the synthesized dancers. (3) Tai Chi performance evaluation (mocap + video): I shall also discuss the feasibility of utilizing spatiotemporal synchronization and, ultimately, machine learning to evaluate Tai Chi routines performed by different subjects in our current project of “Tai Chi + Advanced Technology for Smart Health”.