Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu, Zuyuan; Hu, Huosheng

doi:10.3390/robotics7020017

Cited by 186 publications

(72 citation statements)

References 122 publications

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“…Furthermore, demonstrations are not always optimal, requiring methods to learn from noisy data (Sasaki and Yamashina, 2020). These challenges and others related to learning manipulation from demonstrations are discussed in depth in Zhu and Hu (2018) and Si et al (2021).…”

Section: Learning From Demonstrationsmentioning

confidence: 99%

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Carfì

Patten²,

Kuang³

et al. 2021

Front. Robot. AI

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Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.

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Section: Learning From Demonstrationsmentioning

confidence: 99%

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Carfì

Patten²,

Kuang³

et al. 2021

Front. Robot. AI

View full text Add to dashboard Cite

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“…Mathematically it can be represented as ζ : [0, 1] → x where x ∈ R d and d corresponds to the number of joints with ζ(0) and ζ(1) being the start and goal configurations respectively. These trajectories could be generated by a human expert by demonstrations Zhu and Hu, 2018;Havoutis and Calinon, 2019. As real hardware orbital simulation of micro-gravity environment being extremely expensive, we demonstrate the concept by generating trajectories using an optimal control algorithm Kirk, 2004. We make use of the redundancy of the chosen 7-DoF manipulator arm to generate several trajectories which starts at the home position (Figure 2) and go to a particular goal state given by the vision system.…”

Section: Data Generation For Trajectory Learningmentioning

confidence: 99%

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

et al. 2021

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This paper adds on to the on-going efforts to provide more autonomy to space robots and introduces the concept of programming by demonstration or imitation learning for trajectory planning of manipulators on free-floating spacecraft. A redundant 7-DoF robotic arm is mounted on small spacecraft dedicated for debris removal, on-orbit servicing and assembly, autonomous and rendezvous docking. The motion of robot (or manipulator) arm induces reaction forces on the spacecraft and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action. The method introduced here is capable of finding the trajectory that minimizes the attitudinal changes thereby reducing the load on ADCS. One of the critical elements in spacecraft trajectory planning and control is the power consumption. The approach introduced in this work carry out trajectory learning offline by collecting data from demonstrations and encoding it as a probabilistic distribution of trajectories. The learned trajectory distribution can be used for planning in previously unseen situations by conditioning the probabilistic distribution. Hence almost no power is required for computations after deployment. Sampling from a conditioned distribution provides several possible trajectories from the same start to goal state. To determine the trajectory that minimizes attitudinal changes, a cost term is defined and the trajectory which minimizes this cost is considered the optimal one.

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“…The majority of papers (around 70%) that fall under the "Learning category" were using robot arms and manipulators as their robot platform. This is mainly because the Learning category reviews the learning from demonstration application, which is historically more common for industrial robotics applications in which a user demonstrates the trajectory of the end effector (EE) of a robot arm (Billard et al, 2008;Mylonas et al, 2013;Zhu and Hu, 2018) than in the context of mobile robots (Simões et al, 2020) or aerial robots (Benbihi et al, 2019). On the other hand, around 70% of reviewed papers targeting robot "Perception" applications were using mobile robots.…”

Section: Corotan Andmentioning

confidence: 99%

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

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Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

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Robot Learning from Demonstration in Robotic Assembly: A Survey

Cited by 186 publications

References 122 publications

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

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