644 research outputs found
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics
Research (ISRR) 201
Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents
207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic
Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process
This letter presents a physical human-robot interaction scenario in which a
robot guides and performs the role of a teacher within a defined dance training
framework. A combined cognitive and physical feedback of performance is
proposed for assisting the skill learning process. Direct contact cooperation
has been designed through an adaptive impedance-based controller that adjusts
according to the partner's performance in the task. In measuring performance, a
scoring system has been designed using the concept of progressive teaching
(PT). The system adjusts the difficulty based on the user's number of practices
and performance history. Using the proposed method and a baseline constant
controller, comparative experiments have shown that the PT presents better
performance in the initial stage of skill learning. An analysis of the
subjects' perception of comfort, peace of mind, and robot performance have
shown a significant difference at the p < .01 level, favoring the PT algorithm.Comment: Presented at IEEE International Conference on Robotics and Automation
ICRA-201
A Concept for User-Centered Delegation of Abstract High-Level Tasks to Cobots for Flexible Lot Sizes
Technical advances in collaborative robots (cobots) are making them
increasingly attractive to companies. However, many human operators are not
trained to program complex machines. Instead, humans are used to communicating
with each other on a task-based level rather than through specific
instructions, as is common with machines. The gap between low-level
instruction-based and high-level task-based communication leads to low values
for usability scores of teach pendant programming. As a solution, we propose a
task-based interaction concept that allows human operators to delegate a
complex task to a machine without programming by specifying a task via
triplets. The concept is based on task decomposition and a reasoning system
using a cognitive architecture. The approach is evaluated in an industrial use
case where mineral cast basins have to be sanded by a cobot in a crafts
enterprise
Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents
207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic
Bio-inspired Factories of the Future
The biological transformation of added value is seen as one of the key aspects in applied research. Bio- inspired methods and technologies will affect factories of the future and enable them to cope with changing boundary conditions and the rising necessity of sustainability. This results in a higher demand for flexibility and transformation ability of the comprised production systems.
To elaborate topics like these, Fraunhofer initiated strategic collaborative research projects. The current project aims at developing aspects of the biological transformation, whereof organic bio-inspired factories is one. Different research focal points were identified as enabling technologies on different levels of the well-established automation pyramid. The paper highlights the aspects “facility layout planning”, “behavioral modeling of production systems” and “skill-based controller programming” as enabling technologies. Solution approaches for the addressed aspects are discussed and future steps towards a flexible and sustainable production are shown
Interactive autonomy and robotic skills
Current concepts of robot-supported operations for space laboratories (payload servicing, inspection, repair, and ORU exchange) are mainly based on the concept of 'interactive autonomy' which implies autonomous behavior of the robot according to predefined timelines, predefined sequences of elementary robot operations and within predefined world models supplying geometrical and other information for parameter instantiation on the one hand, and the ability to override and change the predefined course of activities by human intervention on the other hand. Although in principle a very powerful and useful concept, in practice the confinement of the robot to the abstract world models and predefined activities appears to reduce the robot's stability within real world uncertainties and its applicability to non-predefined parts of the world, calling for frequent corrective interaction by the operator, which in itself may be tedious and time-consuming. Methods are presented to improve this situation by incorporating 'robotic skills' into the concept of interactive autonomy
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