Robot programming by non-experts: Intuitiveness and robustness of One-Shot robot programming

Orendt, Eric M.; Fichtner, Myriel; Henrich, Dominik

doi:10.1109/roman.2016.7745110

Cited by 37 publications

(20 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scenarios involving repetition or complex tasks are likely to benefit from having input methods that are facile (6), for instance to provide multiple knot tying demonstrations for LfD [29]. An easy to learn (7) input method may allow non-expert users to confidently (5) provide demonstrations for one-shot learning (e.g., [40]). In Bajcsy et al [20], demonstrations are required in the form of corrections as input for incremental LfD.…”

Section: Characterizing Input Methodsmentioning

confidence: 99%

Characterizing Input Methods for Human-to-Robot Demonstrations

Praveena

Subramani

Mutlu

et al. 2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.

show abstract

Section: Characterizing Input Methodsmentioning

confidence: 99%

Characterizing Input Methods for Human-to-Robot Demonstrations

Praveena

Subramani

Mutlu

et al. 2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

show abstract

“…Orendt et al [9] confirmed that the use of One-Shot Programming by Demonstration (programming a robot with a single demonstration) can be effective and intuitive, especially for end-user participants that successfully accomplished the tasks in the experiment. The results held regardless of the instruction modality.…”

Section: End-user Robot Programming Approachesmentioning

confidence: 97%

Towards Tool-Support for Robot-Assisted Product Creation in Fab Labs

Bergh

Deurzen

Veuskens

et al. 2018

Human-Centered Software Engineering

View full text Add to dashboard Cite

Collaborative robot-assisted production has great potential for high variety low volume production lines. These type of production lines are common in both personal fabrication settings as well as in several types of flexible production lines. Moreover, many assembly tasks are in fact hard to complete by a single user or a single robot, and benefit greatly from a fluent collaboration between both. However, programming such systems is cumbersome, given the wide variation of tasks and the complexity of instructing a robot how it should move and operate in collaboration with a human user. In this paper we explore the case of collaborative assembly for personal fabrication. Based on a CAD model of the envisioned product, our software analyzes how this can be composed from a set of standardized pieces and suggests a series of collaborative assembly steps to complete the product. The proposed tool removes the need for the end-user to perform additional programming of the robot. We use a low-cost robot setup that is accessible and usable for typical personal fabrication activities in Fab Labs and Makerspaces. Participants in a first experimental study testified that our approach leads to a fluent collaborative assembly process. Based on this preliminary evaluation, we present next steps and potential implications.

show abstract

“…Existing PbD approaches try to teach the robot from a small number of demonstrations [3], [17]. We propose an interactive learning approach, where the user can directly modify the learned action models using a user-interface.…”

Section: Overviewmentioning

confidence: 99%

“…It is an intuitive robot programming approach, with the goal to refine the robot's performance, by providing repetitive demonstrations. However, in existing PbD implementations the robot learns an action sequence [3], [4], rather than atomic actions that can be reused independently. Teaching full action sequences is often complicated and time-consuming, as the robot has to be demonstrated a new sequence, whenever the goal changes.…”

mentioning

confidence: 99%

Evaluation of a robot programming framework for non-experts using symbolic planning representations

Liang¹,

Pellier²,

Fiorino³

et al. 2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

Abstract-Cobots (collaborative robots) are revolutionising industries by allowing robots to work in close collaboration with humans. But many companies hesitate their adoption, due to the lack of programming experts. In this work, we evaluate a robot programming framework for non-expert users, that requires users to teach action models expressed in a symbolic planning language (PDDL). These action models would allow the robot to leverage modern automated planners to achieve any userdefined goal. We conducted qualitative user experiments with a Baxter robot to evaluate the non-expert user's understanding of the symbolic planning language and the usability of the framework. We showed that users with little to no programming experience can adopt the symbolic planning language, and use the framework. I. INTRODUCTIONRobots have been working in close collaboration with humans. Cobotic systems [1] have been adopted in several industries from the food-processing industry, to aeronautics, to the health industry. However, many companies remain robot resistant, as they lack programming experts to fully exploit the robots. Programming by Demonstration (PbD) allows non-experts to teach robots new skills by demonstrating a task, without writing any code [2]. It is an intuitive robot programming approach, with the goal to refine the robot's performance, by providing repetitive demonstrations. However, in existing PbD implementations the robot learns an action sequence [3], [4], rather than atomic actions that can be reused independently. Teaching full action sequences is often complicated and time-consuming, as the robot has to be demonstrated a new sequence, whenever the goal changes.In our previous work [5], we addressed the question "Can non-experts teach a robot atomic actions, which can be reused to automatically generate novel action sequences?" We proposed a framework that combines PbD and Automated Planning [6], where the robot learns action models by demonstration, and the problem of finding an action sequence is delegated to a planner. The robot programming process consists of steps: 1) the non-expert user demonstrates atomic actions to the robot, and teaches action models, expressed in a symbolic planning language (STRIPS [7]), 2) the robot uses these action models with an automated planner to generate solutions to user-defined goals, 3) the user can revisit the taught action model to refine them.

show abstract

Robot programming by non-experts: Intuitiveness and robustness of One-Shot robot programming

Cited by 37 publications

References 14 publications

Characterizing Input Methods for Human-to-Robot Demonstrations

Characterizing Input Methods for Human-to-Robot Demonstrations

Towards Tool-Support for Robot-Assisted Product Creation in Fab Labs

Evaluation of a robot programming framework for non-experts using symbolic planning representations

Contact Info

Product

Resources

About