However, the impedance profiles of the human joints vary substantially during motion. Therefore, exoskeletons should accordingly respond and adapt to these impedance profiles. This talk presents methods to develop adaptive impedance control of exoskeletons using biological signals.
Then, an impedance algorithm is proposed transferring stiffness from human operator through the surface electromyography sEMG signals, being utilized to design the optimal reference impedance model.
- Theory of functions of a complex variable.
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In order to verify the proposed approach, the actual implementation has been performed using a real robotic exoskeleton and a human operator. With the advances in robot control RC systems, the relationship between humans and robots has thus become increasingly intimate, and many human-robot collaboration systems have been developed. However, it is hard for a disabled person to operate a robot because of the loss of motion capacity or reduced sensing ability. The target objects are detected by a vision system and then displayed to the user in a video that shows them fused with flicking diamonds.
Through the analysis of the invoked electroencephalograph EEG signals, a brain computer interface BCI is developed to infer the exact object that is required by the user. These results are then transferred to the shared control system, which is enabled by visual servoing VS techniques to achieve accurate object manipulation. Extensive experimental studies are performed to verify the performance of the developed mind control system.
The second wave of Robotics, integrating Machine Learning, Probabilistic Robotics and some AI is already having significant impact on our economy and our society. Meanwhile methodology is lacking, societal and economical impact not well understood, citizen involvement in the issues still too limited.
We need first of all to go back to the basics of the scientific method. The second wave of Robotics, integrating Machine Learning, Probabilistic Robotics, and some AI is already having significant impact on our economy and our society. The third wave inspired by the organizational principles of living beings and natural intelligence and merging more and more tightly with humans will potentially have a disruptive impact on society and our self-perception and very nature. Meanwhile methodology is lacking, societal and economical impact is not well understood, and citizen involvement in the issues still too limited.
Do we need first of all to go back to the basics of the scientific method? This seminar will cover issues about reproducible Robotics research, claim assessment, qualitative result evaluation, benchmarking of the performance of robotic and intelligent systems, and risk modelling. Her current responsibilities include managing the Engineering Laboratory's Robotic Systems for Smart Manufacturing Program, which is focused on advancing the capabilities of agile, collaborative robots through the definition of performance requirements, metrics, test methods, tools, and testbeds.
She is internationally recognized for her work in the development of performance metrics and evaluation methodologies for robotic and autonomous systems.
Benefits of quality management systems
Elena founded key efforts to develop test methodologies for measuring performance of robots, which range from long-term use of robotic competitions to drive innovation to consensus standards for evaluating robotic components and systems. Competitions are a useful tool for measuring robotic system and component performance. Are there certain practices and approaches that may have greater impact? I will discuss some lessons-learned from competitions that may prove useful in selection of metrics and design of tests and benchmarks. In certain cases, conceiving of a competition within a greater ecosystem of innovation can yield greater advancements.
Robot systems and their constituent components, such as sensors and hands, are advancing at what feels like an accelerating pace. This progress is great news, but poses many challenges in being able to understand which robot, algorithm, or components would be appropriate for one's applications. Many claims are made in research papers and product brochures that are hard to translate to a particular real-world prediction of how well a robot would perform.
We will discuss approaches for identifying key performance parameters in order to characterize a robot's performance. One of the key considerations is the fact that performance cannot be discussed in isolation: performance is always contextual. Angel P. He has been invited speaker of 73 plenary speeches, seminars and tutorials, in 15 countries.
He serves as associate or guest editor for 12 journals and has been PI of 37 research projects. Many forecasts predict a dramatic increase in the non-industrial robotics market in the coming years. If they are intended to help in performing daily tasks at home, the perceived quality of service requires a successful physical interaction with the environment.
This poses a number of challenges such as adaptability, autonomy, functionality, resiliency, cost-effectiveness, and safety. I will also look at robots as cyber-physical networked systems and consider the posibilities of cloud computing. I will compare them with robots in online shopping warehouses, with some lessons learned from our participation in the Amazon Robotics Challenge An intelligent robot is a perfect paradigm of a cyber-physical system CPS , since its very nature is based on the seamless integration of computational algorithms and physical components, including embedded sensors, processors and actuators in order to sense and interact with the physical world.
In my speech I will address some of the challenges for robots considered as CPS, such as adaptability, autonomy, functionality, resiliency, and safety, with emphasis on the physical interaction with the environment. As test cases I will consider robots as personal assistants, along with robots in online shopping warehouses, as an example towards the 4th industrial revolution, the so-called Industry 4.
I will also discuss some implications in terms of the interactions of information processing, communication and control of physical processes, with especial emphasis on the difficulties that dealing with open-ended physical entities can bring. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where the focus is on algorithms for human-robot interaction -- algorithms that move beyond the robot's function in isolation, and generate robot behavior that also accounts for interaction and coordination with end-users.
The lab works across different applications, from assistive robots, to manufacturing, to autonomous cars, and draw from optimal control, planning, estimation, learning, and cognitive science. The traditional robotics problem is one of optimization. An engineer writes down a cost function and potentially a set of constraints, thereby specifying what it means for a robot to accomplish its task. The robot then is in charge of finding the behavior that is optimal for this specification.
Thus, the focus in robotics is on how a robot can produce optimal or even feasible behavior despite the intricacies of operating in the real world. What drives my research is the realization that we are not building robots to work in some isolated universe, optimizing some exogenously specified cost function. First, robots will not act in isolation. They will work with and around us.
This makes optimal action in isolation far from sufficient — robots will need to choose actions that mesh well with ours. If there were no people on the roads, autonomous driving would be nearly solved.
Systems for Planning and Control in Manufacturing
Instead, cars need to coordinate with us. So do quadrotors flying in our spaces, or assistive arms in our homes. My work formalizes the problem of optimal coordination with people, and introduces real-time solutions for continuous and high-dimensional state and action spaces. Second, robots will need to do what we want them to. This makes the notion of some exogenously specified cost function a myth. Thus, figuring out how to optimize is only half the battle. The other half is figuring out what to optimize in the first place. And the key to that lies with us, people — what we want is the very definition of the cost function.
My work casts the process of the robot acquiring its cost function as a human-robot collaboration, introducing theory and tools for aligning robot incentives with human preferences and ensuring that robots are resilient to changes in their environment. Jan Peters. Gerhard obtained his Ph.
Wolfgang Mass at the Graz University of Technology. He organized several workshops and is senior program committee for several conferences. In the future, autonomous robots will be used for various applications such as autonomous farming, handling dangerous materials as for example decommissioning nuclear waste, health care or autonomous transportation. In this talk, I will present our work on information-geometric policy search methods for learning complex motor skills. Our algorithms use information-geometric insights to exploit curvature and path information in order to perform efficient local search at the level of single elemental motions, also called movement primitives.
Simultaneously to local search, the algorithms search on a global level by selecting between distinct solutions, allowing us to represent a versatile solution space with high quality solutions. Our algorithms can be used to efficiently learn motor skills, generalize these motions to different situations, learn reactive skills that can react to perturbations and select and learn when to switch between these motions. I will also briefly show how to extend our algorithms to learn from preference-based feedback instead of a numeric reward signal, enabling a human expert to guide the learning agent without the need for manual reward tuning.
While I will use dynamic motor games, such as table tennis, as motivation throughout my talk, I will also shortly present how to apply similar methods for robot grasping and manipulation tasks. His research interests include control algorithms for robotic hands and arms, coordinated hand—arm control, bimanual manipulation, and robotic assembly.
His research focuses on the combination of decision theory and machine learning, motivated by fundamental research questions in robotics. Reoccurring themes in his research are appropriate representations symbols, temporal abstractions, relational representations to enable efficient learning and manipulation in real world environments, and how to achieve jointly geometric, logic and probabilistic learning and reasoning.
There recently is, again, substantial optimism about AI due to the great advances in machine learning and data-driven methods. To this end we need a united machine learning, probabilistic AI and robotics research approach. In this talk I will summarize my work on learning and reasoning in logic, geometric and uncertain domains, and highlight what I think are core research challenges towards real-world AI.
He received the Ph. He was a Postdoctoral Fellow at the M. His research interests broadly include modeling and optimization of operations at various decision levels from real-time to strategic in manufacturing and logistics, with a special emphasis on semiconductor manufacturing.
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He has published more than 65 papers in international journals. He has coordinated multiple academic and industrial research projects. Challenges related to each integration type are then discussed using examples based on academic and industrial research conducted by the author. The integration of decisions in production planning and scheduling and a railway transportation example are discussed for vertical integration. A production planning and vehicle routing problem and a maritime supply chain example are presented for horizontal integration.
I had the opportunity to work for about 14 years on many different projects with two manufacturing sites of the French-Italian semiconductor company STMicroelectronics. Supported by European, national and industrial projects, this still active long-term academic-industrial collaboration led to many scientific and industrial achievements, spreading to other companies. Through regular exchanges, engineers, researchers, PhD and Master students were able to present their problems, their advances and generate new research projects.
After some history of the collaboration, the presentation will survey some of the main research and industrial results in qualification and flexibility management, production and capacity planning, scheduling, automated transportation, dynamic sampling and time constraint management.
Challenges faced and lessons learned when applying Operations Research and Industrial Engineering in practice, and in particular in semiconductor manufacturing, will be discussed.