Industrial robot programming by demonstration

Zhang, Jiafan; Wang, Yue; Xiong, Rong

doi:10.1109/icarm.2016.7606936

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…After the mechanical structure and all components of the robot are installed, the next step is to code the program for the robot, which will then be entered into the robot's micro controller (Zhang et al, 2016). The first step in coding the program is to enter the library of each component into the Arduino IDE software.…”

Section: Making Robot Program Codingmentioning

confidence: 99%

The development of movement system of the second six-legged robot PTE UNIMA

Mamahit

Sanggola

2021

JPTK

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Engineering technology in robotics for the present era is no longer new, especially in high education, marked by Indonesian robot contests routinely held annually by the national achievement center. The participants in this event are groups of students from all higher education institutions spread throughout Indonesia. The development of robotics technology is now faster to spur individuals and students to compete to conduct research and development in robotics. The study aims to develop a six-legged robotic motion system or so-called hexapod. The research was conducted using the Addie model research method consisting of five stages, namely, analyze stage to analyze the needs of the development of the robotic motion system and analysis of the needs of tools and materials to be used. The design stage of designing the mechanical structure of the robot both in terms of hardware and in terms of robot software, the development stage of developing a six-legged robot's motion system to be more stable and more efficient in moving, the implementation stage is a test stage of the robot's motion system that has been developed. The evaluation stage is the last stage of this development research; at this stage, the evaluation is done to ensure the robot's motion system is feasible to use.

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Section: Making Robot Program Codingmentioning

confidence: 99%

The development of movement system of the second six-legged robot PTE UNIMA

Mamahit

Sanggola

2021

JPTK

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“…Some applications already exist that do PbD on the trajectory level (e.g., Emika Franka Panda interface [12]). On the task level [13][14][15][16], demonstrations represent a skill as a sequence of symbolic actions. This paper focuses on the task level, where advice on the assembly will also be viewed as a demonstration.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot

et al. 2019

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The assembly industry is shifting more towards customizable products, or requiring assembly of small batches. This requires a lot of reprogramming, which is expensive because a specialized engineer is required. It would be an improvement if untrained workers could help a cobot to learn an assembly sequence by giving advice. Learning an assembly sequence is a hard task for a cobot, because the solution space increases drastically when the complexity of the task increases. This work introduces a novel method where human knowledge is used to reduce this solution space, and as a result increases the learning speed. The method proposed is the IRL-PBRS method, which uses Interactive Reinforcement Learning (IRL) to learn from human advice in an interactive way, and uses Potential Based Reward Shaping (PBRS), in a simulated environment, to focus learning on a smaller part of the solution space. The method was compared in simulation to two other feedback strategies. The results show that IRL-PBRS converges more quickly to a valid assembly sequence policy and does this with the fewest human interactions. Finally, a use case is presented where participants were asked to program an assembly task. Here, the results show that IRL-PBRS learns quickly enough to keep up with advice given by a user, and is able to adapt online to a changing knowledge base.

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“…These methods are: tele-operation with joystick or different other interfaces (Forbes et al 2015;Pathirage et al 2013), kinesthetic teaching (Kormushev et al 2010;Kober and Peters 2009), wearable sensors placed on the teacher' body (Krug and Dimitrovz 2013;Leitner et al 2014), and vision-based external sensors (Quintero et al 2014;Zhang et al 2016).…”

Section: Related Workmentioning

confidence: 99%

“…In order to make industrial robot programming easier, several technologies have been used in past years. One of them is a framework that enables human collaborators to simply demonstrate the task to the robot as opposite to time consuming and high technical skills demanding traditional robot programming methods (Zhang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Robot learning of industrial assembly task via human demonstrations

et al. 2018

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Human-robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a task in a not fully repetitive manner using their expertise and cognitive capabilities. The traditional robot programming cannot cope with these challenges of human-robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. Through the demonstrations, the robot learns the sequence of actions for an assembly task (high-level learning) without the need of pre-programming. Additionally, the robot learns every path as needed for object manipulation (low-level learning). Once the robot has the knowledge of the demonstrated task, it can perform the task in collaboration with the human. However, the need for adaptation of the learned knowledge may arise as the human collaborator could introduce changes in the environment, such as placing an object to be manipulated in a position and orientation different from the demonstrated ones. In this paper, a novel real-time adaptation algorithm to cope with these changes in the environment, introduced by the human factor, is proposed. The proposed algorithm is able to identify the sequence of actions needed to be performed in a new environment. A Gaussian Mixture Model-based modification algorithm is able to adapt the learned path in order to enable robot to successfully complete the task without the need of additional training by demonstration. The proposed framework copes with changes in the position and orientation of the objects to be manipulated and also provides obstacle avoidance. Moreover, the framework enables the human collaborator to suggest different sequence of actions for the learned task, which will be performed by the robot. The proposed algorithm was tested on a dual-arm industrial robot in an assembly scenario and the results are presented. Shown results demonstrate a potential of the proposed robot learning framework to enable continuous human-robot collaboration.

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Industrial robot programming by demonstration

Cited by 14 publications

References 19 publications

The development of movement system of the second six-legged robot PTE UNIMA

The development of movement system of the second six-legged robot PTE UNIMA

Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot

Robot learning of industrial assembly task via human demonstrations

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