Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div

Manufacturing Technology Today

Manufacturing Technology Today, Manufacturing Technology Abstracts, Vol. 14, No. 4, September 2015, Bangalore, India

Basic set of behaviours for programming assembly robots

We know from the well established Church-Turing thesis that any computer program­ming language needs just a limited set of commands in order to perform any computable process. However, programming in these terms is so very inconvenient that a larger set of machine codes need to be introduced and on top of these higher programming languages are erected.In Assembly Robotics we could theoretically formulate any assembly task, in terms of moves. Nevertheless, it is as tedious and error prone to program assemblies at this low level as it would be to program a computer by using just Turing Machine commands.An interesting survey carried out in the beginning of the nineties showed that the most common assembly operations in manufacturing industry cluster in just seven classes. Since the research conducted in this thesis is developed within the behaviour-based assembly paradigm which views every assembly task as the external manifestation of the execution of a behavioural module, we wonder whether there exists a limited and ergonomical set of elementary modules with which to program at least 80% of the most common operations.IIn order to investigate such a problem, we set a project in which, taking into account the statistics of the aforementioned survey, we analyze the experimental behavioural decomposition of three significant assembly tasks (two similar benchmarks, the STRASS assembly, and a family of torches). From these three we establish a basic set of such modules.The three test assemblies with which we ran the experiments can not possibly exhaust ah the manufacturing assembly tasks occurring in industry, nor can the results gathered or the speculations made represent a theoretical proof of the existence of the basic set. They simply show that it is possible to formulate different assembly tasks in terms of a small set of about 10 modules, which may be regarded as an embryo of a basic set of elementary modules.Comparing this set with Kondoleon’s tasks and with Balch’s general-purpose robot routines, we observed that ours was general enough to represent 80% of the most com­mon manufacturing assembly tasks and ergonomical enough to be easily used by human operators or automatic planners. A final discussion shows that it would be possible to base an assembly programming language on this kind of set of basic behavioural modules

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Investigating Precise Control in Spatial Interactions: Proxemics, Kinesthetics, and Analytics

Augmented and Virtual Reality (AR/VR) technologies have reshaped the way in which we perceive the virtual world. In fact, recent technological advancements provide experiences that make the physical and virtual worlds almost indistinguishable. However, the physical world affords subtle sensorimotor cues which we subconsciously utilize to perform simple and complex tasks in our daily lives. The lack of this affordance in existing AR/VR systems makes it difficult for their mainstream adoption over conventional $2D$ user interfaces. As a case in point, existing spatial user interfaces (SUI) lack the intuition to perform tasks in a manner that is perceptually familiar to the physical world. The broader goal of this dissertation lies in facilitating an intuitive spatial manipulation experience, specifically for motor control. We begin by investigating the role of proximity to an action on precise motor control in spatial tasks. We do so by introducing a new SUI called the Clock-Maker's Work-Space (CMWS), with the goal of enabling precise actions close to the body, akin to the physical world. On evaluating our setup in comparison to conventional mixed-reality interfaces, we find CMWS to afford precise actions for bi-manual spatial tasks. We further compare our SUI with a physical manipulation task and observe similarities in user behavior across both tasks. We subsequently narrow our focus on studying precise spatial rotation. We utilize haptics, specifically force-feedback (kinesthetics) for augmenting fine motor control in spatial rotational task. By designing three kinesthetic rotation metaphors, we evaluate precise rotational control with and without haptic feedback for 3D shape manipulation. Our results show that haptics-based rotation algorithms allow for precise motor control in 3D space, also, help reduce hand fatigue. In order to understand precise control in its truest form, we investigate orthopedic surgery training from the point of analyzing bone-drilling tasks. We designed a hybrid physical-virtual simulator for bone-drilling training and collected physical data for analyzing precise drilling action. We also developed a Laplacian based performance metric to help expert surgeons evaluate the resident training progress across successive years of orthopedic residency