Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Advantages and limitations of reservoir computing on model learning for robot control

In certain cases analytical derivation of physicsbased models of robots is difficult or even impossible. A potential workaround is the approximation of robot models fromsensor data-streams employing machine learning approaches.In this paper, the inverse dynamics models are learned byemploying a learning algorithm, introduced in [1], which isbased on reservoir computing in conjunction with self-organizedlearning and Bayesian inference. The algorithm is evaluatedand compared to other state of the art algorithms in termsof generalization ability, convergence and adaptability usingfive datasets gathered from four robots in order to investigateits pros and cons. Results show that the proposed algorithmcan adapt in real-time changes of the inverse dynamics modelsignificantly better than the other state of the art algorithms

A Roadmap Towards Intelligent and Autonomous Object Manipulation for assembly Tasks

Despite the large scientific interest on robot learning forobject picking tasks, the research on object placingis too limited. Commonly, placing is simplistically consideredas a trivial task, but real life manipulation problems indicatethe exact opposite. A placing task can have different levels ofcomplexity, ranging from the simplest tabletop placing of anobject, to more complex cases such as loading a dishwasherand assembling industrial parts. In this paper we argue thatassembly can and needs to be seen as a complex placing task.Thus, the need for systems with advanced placing capabilitiesbecomes evident.We consider mac

Recent Advances in Robot Learning from Demonstration

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated.While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice