We are partners in several projects and also manage a thematic programme for PASCAL2. In the past at MPI, we have also been part of PASCAL2 Pump Priming Project with Koby Crammer (Technion). Inside TU Darmstadt, we are part of the MoTaSyS project.
Within 3AI, we aim at developing the next generation of AI systems. Pushing the limits of hybrid and neuro-symbolic AI, these AI systems will acquire human-like communication and thinking abilities, recognize and classify new situations, and adapt to them autonomously. Through 3AI, the systemic and algorithmic foundations of what we call “Systems AI” will be developed. Akin to Systems Biology, interactions of different AI building blocks will be mathematically and algorithmically correctly captured, understood, and used, and new methods of system design, e.g., software engineering or data management for Systems AI, will be explored.
The Adaptive Mind One of the most critical challenges facing any organism is to maintain stability in the face of a dynamic and uncertain world. At the same time, successful behavior also rests crucially on our ability to adapt when circumstances fundamentally change. These two conflicting demands present a major dilemma: to determine when variability is noise that must be suppressed, and when it is signal that requires adjusting our behavior to the ‘new normal’. Understanding how we resolve this Stability-Transition dilemma is central to understanding the adaptive mind and is one of the most significant open questions in science. It occurs at every level of human thought and behavior—from low-level sensory adaptation to long-term changes when practicing advanced skills. Resolving the stability-transition dilemma is crucial to survival: it defines our success, and its failure may cause mental disorder. In The Adaptive Mind, we combine rigorous behavioral research methods and theories from experimental psychology, with the unique patient-oriented insights of psychiatry and clinical psychology and the power of quantitative analysis and computational modeling by artificial intelligence. Our goal is to observe empirically, describe quantitatively, and model computationally how the human mind continually adapts in an ever-changing and often unpredictable world. By examining stability and transition, we seek to characterize one of the canonical computations of the human mind—adaptation—that links basic sensory and motor processes with high-level aspects of cognition and behavior across the entire lifespan, in both healthy individuals and those suffering from paradigmatic mental disorders. To do so, we will create a collaborative research program with five tightly interwoven Key Areas (Dynamics, Context, Interaction, Skills and Disorder), and a Data Hub for mining and sharing data, to investigate the adaptive mind as it performs complex natural behaviors. Only by integrating insights and methods from empirical researchers, theoreticians and clinicians can we develop a comprehensive understanding of the adaptive mind.
Until a few years ago, intelligent systems such as robots and digital voice assistants had to be tailored towards narrow and specific tasks and contexts. Such systems needed to be programmed and fine tuned by experts. But, recent developments in artificial intelligence have led to a paradigm shift: instead of explicitly representing knowledge about all information processing steps at time of development, machines are endowed with the ability to learn. With the help of machine learning it is possible to leverage large amounts of data samples, which hopefully transfer to new situations via pattern matching. Groundbreaking achievements in performance have been obtained over the last years with deep neural networks, whose functionality is inspired by the structure of the human brain. A large number of artificial neurons interconnected and organized in layers process input data under large computational costs. Although experts understand the inner working of such systems, as they have designed the learning algorithms, often they are not able to explain or predict the system’s intelligent behavior due to its complexity. Such systems end up as blackboxes raising the question of how such systems’ decisions can be understood and trusted. Our basic hypothesis is that explaining an artificial intelligence system may not be fundamentally different from the task of explaining intelligent goal-directed behavior in humans. Behavior of a biological agent is also based on the information processing of a large number of neurons within brains and acquired experience. But, an explanation based on a complete wiring diagram of the brain and all its interactions with its environment may not provide an understandable explanation. Instead, explanations of intelligent behavior need to reside at a computationally more abstract level: they need to be cognitive explanations. Such explanations are developed in computational cognitive science. Thus, WhiteBox aims at transforming blackbox models into developing whitebox models through cognitive explanations that are interpretable and understandable. Following our basic assumption, we will systematically develop and compare whitebox and blackbox models for artificial intelligence and human behavior. In order to quantify the differences between these models, we will not only develop novel blackbox and whitebox models, but also generate methods for the quantitative and interpretable comparison between these models. Particularly, we will develop new methodologies to generate explanations automatically by means of AI. As an example, deep blackbox models comprise deep neural networks whereas whitebox models can be probabilistic generative models with explicit and interpretable latent variables. Application of these techniques to intelligent goal directed human behavior will provide better computational explanations of human intelligent behavior as well as allow to transfer human level behavior to machines.
The goal of SKILLS4ROBOTS is to develop an autonomous skill learning system that enables humanoid robots to acquire and improve a rich set of motor skills. This robot skill learning system will allow scaling of motor abilities up to fully anthropomorphic robots while overcoming the current limitations of skill learning systems to only few degrees of freedom. To achieve this goal, it will decompose complex motor skills into simpler elemental movements - called movement primitives - that serve as building blocks for the higher-level movement strategy and the resulting architecture will be able to address arbitrary, highly complex tasks -- up to robot table tennis for a humanoid robot. Learned primitives will be superimposed, sequenced and blended. For example, a game of robot table tennis can be represented using different stroke movement primitives, such as a forehand stroke, a backhand stroke or a smash, as well as locomotion primitives for foot placement for maintaining balance by shifting the center of mass of the robot. The resulting decomposition into building blocks is not only inherent to many motor tasks but also highly scalable and will be exploited by our learning system. Four recent breakthroughs in our research will make this project possible due to successes on the representation of the parametric probabilistic representations of the elementary movements, on probabilistic imitation learning, on relative entropy policy search-based reinforcement learning and on the modular organization of the representation. These breakthroughs will allow create a general, autonomous skill learning system that can learn many different skills in the exact same framework without changing a single line of programmed code.
To make possible such an embodied tele-operated robotic system for dexterous manipulation, thus without any assumption about the object to be manipulated and the operating environment, the CHIRON project features the unique innovations simultaneously on robotics, computer vision and machine learning. Specifically, the CHIRON project will make use of a dual-arm robot and aims to bring breakthroughs in the following research topics: compliant grippers with tactile feedback, deep understanding of the scene, reinforcement learning-based shared robot control, intuitive and effective haptic interface for the embodiment of the dual-arm robot, and the last and not the least few shot learning given the very limited amount of data, e.g., trials that the envisaged system can afford with the physical ability limited human operator, for the training of the deep scene analysis and shared robot control so that the embodied dual-arm robot easily to adapt to novel operator for complex never seen before object manipulation in rapidly changing environments. As such, the CHIRON project fits perfectly the objectives of this trilateral call for proposals on AI, specifically in “advancing the state of the art in AI in order to accomplish complex tasks”; and “allowing high-level interactions with human users” and contributing in core AI technologies. The key components required by the CHIRON project are covered with the unique symbiosis of the respective world class expertise of each partner. Prof.Hasegawa’s group from Japan brings its unique expertise in assistive robotics with embodiment for augmented human physical skills, e.g., extra robotic thumb, intelligent cane for elderly, exoskeleton. Prof. Peters’ team from Germany is providing their well known rich experience in robotc manipulation, including tactile sensing, robotic tele-operation, learning by demonstration, and reinforcement learning. Prof. Chen’s group from France is bringing their confirmed expertise in computer vision and machine learning for deep understanding of the scene for object manipulation.
The goal of the ROBOLEAP (Robot learning to perceive, plan, and act under uncertainty) project is to develop reinforcement learning methods that allow robots to operate in unstructured partially observable real world environments found in household robotics, adaptive manufacturing, elderly care, handling dangerous materials, or even disaster scenarios such as Fukujima. Robots that can operate in such complex environments need data-driven reinforcement learning methods that can take uncertainty due to partial observability into account. To make reinforcement learning in partially observable robotic tasks feasible we will develop new memory representations which allow us to efficiently reuse experience with different kinds of policies. To enable long-term action selection, we will improve exploration and value propagation over long horizons: under partial observability the robot needs to execute information gathering actions which requires uncovering and propagating values over long horizons during policy optimization. Moreover, in partially observable settings the problem of assigning values to actions is amplified. To solve this problem we will give the robot additional side information during learning. We will evaluate these methodological advances by endowing a real robot with the ability to play Mikado, a task that exhibits all the main difficulties connected to partial observability. The robot has to deal with occlusions and partial information. It has to proactively test physical properties of Mikado sticks and integrate this knowledge into its manipulation skills to remove sticks from the heap.
SHAREWORK‘s main objective is to endow an industrial work environment of the necessary »intelligence« and methods for the effective adoption of Human Robot Collaboration (HRC) with not fences, providing a system capable of understanding the environment and human actions through knowledge and sensors, future state predictions and with the ability to make a robot act accordingly while human safety is guaranteed and the human-related barriers are overcome. SHAREWORK will develop the needed technology for facing the new production paradigm compiling the necessary developments in a set of modular hardware, software and procedures to face different HRC applications in a systematic and effective way. A knowledge base (KB) to include system »know-how« data as well as real-time environment information is developed. An environment run-time perception and cognition updates this KB with object detection, human tracking and task identification. A human-aware dynamic task planning system will react based on previous knowledge and environment status by reassigning tasks and/or reconfiguring robot control. This data will allow robot intelligent motion planners to control robots while safety is ensured by a continuous ergonomics and risk assessment module to face a safetyproductivity trade-off. A multimodal human-robot communication system will provide interfaces for bidirectional communication between operator and robot. Finally, methods for overcoming human-related barriers and data reliability and security concerning the entire framework are applied for a successful integration in the industry. SHAREWORK technology will be demonstrated in four different industrial cases: for railway, automotive, mechanical machining and equipment goods sectors. The usability of the developed HRC solutions in different industrial sectors and company sizes will increase productivity, flexibility, and reduce human stress, to support the workers and to strengthen European industry.
This project aims to develop a new paradigm to build open-ended learning robots called Goal-based Openended Autonomous Learning (GOAL). GOAL rests upon two key insights. First, to exhibit an autonomous open-ended learning process, robots should be able to self-generate goals, and hence tasks to practice. Second, new learning algorithms can leverage self-generated goals to dramatically accelerate skill learning. The new paradigm will allow robots to acquire a large repertoire of flexible skills in conditions unforeseeable at design time with little human intervention, and then to exploit these skills to efficiently solve new user-defined tasks with no/little additional learning. This innovation will be essential in the design of future service robots addressing pressing societal needs. The project will develop the GOAL paradigm by pursuing three main objectives: (1) advance our understanding of how goals are formed and underlie skill learning in children; (2) develop innovative computational architectures and algorithms supporting (2a) the self-generation of useful goals based on user/task independent mechanisms such as intrinsic motivations, and (2b) the use of such goals to efficiently and autonomously build large repertoires of skills; (3) demonstrate the potential of GOAL with a series of increasingly challenging demonstrators in which robots will autonomously develop complex skills and use them to solve difficult challenges in real-life scenarios. The interdisciplinary project consortium is formed by leading international roboticists, computational modelers, and developmental psychologists working with complementary approaches. This will allow the project to greatly advance our understanding of the fundamental principles of open-ended learning and to produce a breakthrough in the field of autonomous robotics by producing for the first time robots that can autonomously accumulate complex skills and knowledge in a truly open-ended way.
The use of internal physical models allows creating training data without a large number of real-world explorations. Such a reduction of the dependency on real-world samples alleviates one of the key problems of Reinforcement Learning: complex problems require a large number of explorations which rapidly becomes unmanageable when moving to a larger dimensionality. The usage of internal simulations allows creating big data, and creating training input with a large number of variations / parameter distributions. The results opens the door to learning approaches that rely on large amounts of data. This project is a collaboration with Michael Gienger and his team at Honda Research Institute at Offenbach, Germany.
Machine learning and artificial intelligence have made important and substantial progress in recent years. By now, they are reaching large scale applications in industrial environments. Such machine learning methods increasingly enable unforeseen development of new applications of technical systems and robots. Many large companies, such as Google, Baidu and Microsoft, are heavily investing into integrating such technology in their products and providing a new, better user experience. While perception tasks -- such as hearing and seeing -- have been particularly difficult for machines for a long time, we have seen an enormous improvement in this field thanks to machine learning in recent years. For example, by using techniques from the field of "deep learning", machines are already reaching and overtaking human performance for specific tasks (e.g., skin cancer recognition).
In this research project, the required work for a Ph.D. thesis in reinforcement learning in the context of industrial robotics is to be pursued. This topic may well change the role of robots in many manufacturing processes. Instead of being manually programmed to carry out the same task millions of times, future robots would be enabled to adapt to hundreds of different tasks autonomously and could become useful for small series production. The proposed research will develop reinforcement learning methods for more complex robot activities enabling as well as interaction with humans. The Ph.D. project is funded by the BOSCH FORSCHUNGSSTIFTUNG (Bosch Research Foundation).
The overall objective of the KoBo34 project is to contribute to improving the social participation of elderly people and maintaining their independence with a humanoid service robot that will be developed in KoBo34. The determination of useful activities to be supported by robotics technology and the technical design of these options at the cutting edge of today's scientific knowledge and technical possibilities are central to the project, as well as the processing and consideration of the acceptance requirements and the comprehensive evaluation of the Implementation. In close collaboration with the center for cognitive science the IAS research within KoBO34 focusses on "Intentional recognition and interaction learning", which is the (mutual) recognition and coordination of the movement and action intentions of human and robot. Here, "physical" interaction and communication between robot and human, e.g. physical feedback through touch or motion gestures, as well as the haptic, interactive training of complex procedures with non-expert users is an interesting and challenging research aspect.
The goal of this project is to develop a hierarchical learning system that decomposes complex motor skills into simpler elemental movements, also called movement primitives, that serve as building blocks of our movement strategy. For example, in a tennis game, such primitives can represent different tennis strokes such as a forehand stroke, a backhand stroke or a smash. As we can see, the autonomous decomposition into building blocks is inherent to many motor tasks. In this project, we want to exploit this basic structure for our learning system. To do so, our autonomous learning system has to extract the movement primitives out of observed trajectories, learn to generalize the primitives to different situations and select between, sequence or combine the movement primitives such that complex behavior can be synthesized out of the primitive building blocks. Our autonomous learning system will be applicable to learning from demonstrations as well as subsequent self improvement by reinforcement learning. Learning will take place on several layers of the hierarchy. While on the upper level, the activation policy of different primitives will be learned, the intermediate level of the hierarchy extracts meta-parameters of the primitives and autonomously learns how to adapt these parameters to the current situation. The lowest level of the hierarchy learns the control policies of the single primitives. Learning on all layers as well as the extraction of the structure of the hierarchical policy is aimed to operate with a minimal amount of dependence from a human expert. We will evaluate our autonomous learning framework on a robot table tennis platform, which will give us many insights in the hierarchical structure of complex motor tasks.
Over the course of the last decade, the framework of reinforcement learning (RL) has developed into a promising tool for learning a large variety of different tasks in robotics. During this timeframe, a lot of progress has been made towards scaling reinforcement learning to high-dimensional systems and solving tasks of increasing complexity. Unfortunately, this scalability has been achieved by using expert knowledge to pre-structure the learning problem in several dimensions. As a consequence, the state-of-the-art methods in robot reinforcement learning generally depend on hand-crafted state representations, pre-structured parametrized policies, well-shaped reward functions and demonstrations by a human expert to aid scaling of the learning algorithm. This large amount of required pre-structuring arguably is in stark contrast to the goal of developing autonomous reinforcement learning systems. In this project, we want to advance the field by starting with a 'classical' reinforcement learning setting for a challenging robotic task (i.e., tetherball). Solving this task by RL methods will be already a valuable contribution. From there on, we will start to identify the components for which the learning task design still needs engineering experience. In the course of this project, we show how we aim to drive each of these components towards more autonomy while developing highly scalable approaches.
The RoMaNS (Robotic Manipulation for Nuclear Sort and Segregation) project will advance the state of the art in mixed autonomy for tele-manipulation, to solve a challenging and safety-critical “sort and segregate” industrial problem, driven by urgent market and societal needs. Cleaning up the past half century of nuclear waste represents the largest environmental remediation project in the whole of Europe. Nuclear waste must be “sorted and segregated”, so that low-level contaminated waste is placed in low-level storage containers, rather than occupying extremely expensive and resource intensive high-level storage containers and facilities. Many older nuclear sites (>60 years in UK) contain large numbers of legacy storage containers, some of which have contents of mixed contamination levels, and sometimes unknown contents. Several million of these legacy waste containers must now be cut open, investigated, and their contents sorted. This can only be done remotely using robots, because of the high levels of radioactive material. Current state-of-the-art practice in the industry, consists of simple tele-operation (e.g. by joystick or teach-pendant). Such an approach is not viable in the long- term, because it is prohibitively slow for processing the vast quantity of material required. The project will: 1) Develop novel hardware and software solutions for advanced bi-lateral master-slave tele-operation. 2) Develop advanced autonomy methods for highly adaptive automatic grasping and manipulation actions. 3) Combine autonomy and tele-operation methods using state-of-the-art understanding of mixed initiative planning, variable autonomy and shared control approaches.
TACMAN addresses the key problem of developing an information processing and control technology enabling robot hands to exploit tactile sensitivity and thus become as dexterous as human hands. The current availability of the required technology now allows us to considerably advance in-hand manipulation. TACMAN’s goal is to develop fundamentally new approaches which can replace manual labor under inhumane conditions by endowing robots with such tactile manipulation abilities, by transferring insights from human neuroscientific studies into machine learning algorithms. TACMAN will provide an innovative new technology that is key for bringing industrial manufacturing back to Europe. Consider the case of the iPhone, where most mechanical manipulation of the major components is achieved by manual human labor under terrible work conditions and not by advanced industrial robots—despite that millions of iPhones are industrially assembled per month. The reason for this absence of appropriate automation is the lack of manipulation skills of current robots. Commercially available robotic hand-arm systems move more accurately and faster than humans, and their sensors see more and at a higher precision—even the smallest forces and torques can be detected. Despite these impressive sensori-motor abilities, current robots are terrible at manipulation when compared to humans. Neuro- science provides a clear reason for the superiority of human hands: During manipulation, humans make substantial use of the data from tactile sensors, i.e., the information obtained through the feeling in the human’s fingers. Robot hands are lacking this key ability! Hence, the rationale of TACMAN is that this performance gap in manipulation ability can be filled by (1) making such tactile sensory comprehensible, and (2) use the information provided by such sensors intelligently for behavior generation. TACMAN aims to integrate the most robust available tactile sensors into the control of existing modern robot hands, and, based on this control law, develop tactile sensor-based manipulation solutions. To make this innovation tractable in a three year project, we aim only on recognising and handling objects that are already in the hand. The structure of the project is designed to allow quick scaling from straightforward, well-captured scenarios employing a single finger to complex multi-fingered manipulation.
Robot manipulation is commonly conceived as a high-potential future business area due to the numerous potential applications. Among them are factory assembly, medical applications, service robotics, offshore robotics, disaster robot applications and others. This project will create new concepts and techniques for robot learning of manipulation skills from a human teacher. In recent and current work, we are investigating movement representations and learning of simple movements, which we represent in so called Movement Primitives. The particular focus of this joint project with the Honda Research Institute at Offenbach, Germany, is to learn the coordination of such primitives, in order to realize complex sequential and parallel movement behaviour. An illustrative example is the replacement of a light bulb: The robot’s movement skill can be composed of elementary primitives, such as reaching towards the lamp, aligning the fingers with the bulb, grasping the bulb or turning it in the thread. The sequential skill is coordinating these primitives with a flexible arbitration scheme: It needs to maintain the causal order of the primitives (e.g. reach – pre-shape – grasp), while coordinating the timing of primitives that are active in parallel (co-articulation of left and right hand for bi-manual skills). In case of larger disturbances, the skill needs to adapt the sequential flow to account for the changed situation (e.g. pick up bulb if it drops out of the hand). This project is a collaboration with Michael Gienger and his team at Honda Research Institute at Offenbach, Germany.
Robots have been essential for keeping industrial manufacturing in Europe. Most factories have large numbers of robots in a fixed setup and few programs that produce the exact same product hundreds of thousands times. The only common interaction between the robot and the human worker has become the so-called “emergency stop button”. As a result, re-programming robots for new or personalized products has become a key bottleneck for keeping manufacturing jobs in Europe. The core requirement to date has been the production in large numbers or at a high price. Robot-based small series production requires a major breakthrough in robotics: the development of a new class of semi-autonomous robots that can decrease this cost substantially. Such robots need to be aware of the human worker, alleviating him from the monotonous repetitive tasks while keeping him in the loop where his intelligence makes a substantial difference. In the 3rd Hand project, we pursue this breakthrough by developing a semi-autonomous robot assistant that acts as a third hand of a human worker. It will be straightforward to instruct even by an untrained layman worker, allow for efficient knowledge transfer between tasks and enable a effective collaboration between a human worker with a robot third hand. The main contributions of this project will be the scientific principles of semi-autonomous human-robot collaboration, a new semi-autonomous robotic system that is able to: i) learn cooperative tasks from demonstration; ii) learn from instruction; and iii) transfer knowledge between tasks and environments. We will demonstrate its efficiency in the collaborative assembly of an IKEA-like shelf where the robot acts as a semi-autonomous 3rd-Hand.
The CoDyCo project is an EU STREP project centered on "Whole-body Compliant Dynamical Contacts in Cognitive Humanoids". The aim of CoDyCo is to advance the current control and cognitive understanding about robust, goaldirected whole-body motion interaction with multiple contacts. CoDyCo will go beyond traditional approaches: (1) proposing methodologies for performing coordinated interaction tasks with complex systems; (2) combining planning and compliance to deal with predictable and unpredictable events and contacts; (3) validating theoretical advances in real-world interaction scenarios. First, CoDyCo will advance the state-of-the-art in the way robots coordinate physical interaction and physical mobility. Traditional industrial applications involve robots with limited mobility. Consequently, interaction (e.g. manipulation) was treated separately from whole-body posture (e.g. balancing), assuming the robot firmly connected to the ground. Foreseen applications involve robots with augmented autonomy and physical mobility. Within this novel context, physical interaction influences stability and balance. To allow robots to surpass barriers between interaction and posture control, CoDyCo will be grounded in principles governing whole-body coordination with contact dynamics. Second, CoDyCo will go beyond traditional approaches in dealing with all perceptual and motor aspects of physical interaction, unpredictability included. Recent developments in compliant actuation and touch sensing allow safe and robust physical interaction from unexpected contact including humans. The next advancement for cognitive robots, however, is the ability not only to cope with unpredictable contact, but also to exploit predictable contact in ways that will assist in goal achievement. Third, the achievement of the project objectives will be validated in real-world scenarios with the iCub humanoid robot engaged in whole-body goal-directed tasks. The evaluations will show the iCub exploiting rigid supportive contacts, learning to compensate for compliant contacts, and utilizing assistive physical interaction
The CompLACS project is also an EU STREP project which can be described as follows: Cognitive architectures capable of operating autonomously in complex environments will require a constant interaction with this environment (e.g. with multiple users, in the case of web agents) and a high degree of modularity (e.g. user profiling module interacting with text generation modules, or recommendation systems, for example). Understanding the behavior of complex adaptive systems, where multiple parts are both driven by data and co-adapting, is a key question for the design of real world intelligent cognitive systems, that is "Composing learning systems for Artificial Cognitive Systems" or CompLACS. The project aims to develop key enabling machine learning technologies necessary for building artificial cognitive systems as well as developing a principled method of breaking down cognitive system design into well-specified components that can be matched against specified sub-systems together with guarantees on the behaviour of the resulting composition.
The Robot Interaction Learning of Cooperative and Competitive Actions (RILCCA) project is funded by a grant of the Daimler-and-Benz Foundation for Postdocs and Juniorprofessors, and can be described as follows: In this project, we will develop new algorithms that allow anthropomorphic robots to learn how to engage in joint actions with a human partner in order to learn manipulation tasks. The focus lies on learning models-of-interaction from observed data, e.g., from a recorded rapport between two persons. Using optical tracking technology the movements of a pair of persons are first recorded and then processed using machine learning algorithms. The result is a model of how each person adapted his or her behavior to the the movements of the respective other. Once a model is learned, it can be used by a robot to engage in a similar interaction with a human counter part. For example, by observing how two workmen collaborate on a maintenance task using a motion tracking setup, a robot can learn what actions and responses are needed to assist in a similar maintenance task.
The GeRT project is an EU STREP project. GeRT stands for Generalizing Robot manipulation Tasks. Its' goal is to enable a robot to autonomously generalize its manipulation skills from known objects to previously unmanipulated objects in order to achieve everyday manipulation tasks. To achieve this aim, GeRT employs a set of demonstration programs for the same abstract task with different objects and varying scene arrangements. These programs are coded by hand and executed on the robotic system. The results from these example programs form the base for generalizing the planning operators and for learning pre and post conditions of operations. We are part of WP3 and WP4 as well as leader for WP5.