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Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots Considering Temporal Body Changes (2405.11798v1)

Published 20 May 2024 in cs.RO

Abstract: In this study, we investigate object grasping by visual servoing in a low-rigidity robot. It is difficult for a low-rigidity robot to handle its own body as intended compared to a rigid robot, and calibration between vision and body takes some time. In addition, the robot must constantly adapt to changes in its body, such as the change in camera position and change in joints due to aging. Therefore, we develop a method for a low-rigidity robot to autonomously learn visual servoing of its body. We also develop a mechanism that can adaptively change its visual servoing according to temporal body changes. We apply our method to a low-rigidity 6-axis arm, MyCobot, and confirm its effectiveness by conducting object grasping experiments based on visual servoing.

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Citations (4)
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Summary

  • The paper presents a novel self-supervised approach that integrates parametric bias into a visual servoing network to adapt to temporal changes.
  • The method uses repetitive, self-directed object manipulation tasks to enhance calibration and efficiency in low-rigidity robots.
  • Experimental results confirm that the VSNPB model maintains high grasp accuracy despite mechanical variations, underlining its potential for flexible robotics.

Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots

The paper delineated in "Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots Considering Temporal Body Changes" is predicated upon developing methodologies to enhance robotic flexibility, specifically for systems composed of low-rigidity materials. The system facilitates autonomous learning for low-rigidity robots, which are commonly plagued by calibration challenges owing to body compliance. This compliance makes the conventional model training and application of robot perception to action a complex affair, requiring constant recalibration to accommodate shifts in the robot's kinematic chain due to mechanical wear and tear or even operational shifts.

The authors' emphasis is on leveraging the inherent ability of low-rigidity robots to model their body's visual servoing mechanisms autonomously. They present a paradigm whereby a robot, MyCobot in this case, can engage in self-supervised learning, thus negating the need for human intervention in calibration and teaching of the robot's tasks, which is a merit considering operational scalability and efficiency. The paper introduces parametric bias (PB) to the learning mechanism, allowing the model to adapt dynamically to temporal changes in the robot's body, promoting the ability to respond effectively to cumulative structural alterations.

A pivotal facet of the paper is the novel data collection strategy wherein the robot executes a self-directed task of placing and picking up an object without external demonstrations or reliance on reinforcement learning. This strategy of using repetitive task execution as a learning mechanism allows the robot to consistently and dynamically align its perceived visual data with actual physical transformations. The robot can perform accurate object grasping, leveraging the high repeatability of its movements to appreciate the temporal changes in its joint configurations and visual perceptions.

In detail, the research employs a visual servoing network with parametric bias (VSNPB), an iterative closed-loop system integrating the robot's joint states and visual inputs to refine function over time. This system is handily adaptable to low-rigidity configurations due to its capacity to embed dynamic modeling errors arising from temporal body changes directly into the bias input, facilitating real-time adaptation.

The experimental results underscore the capacity of the VSNPB model to manage evolving body dynamics efficiently. With multiple body states (comprising alignment variations and camera position shifts), the network exhibits resilience in maintaining grasp accuracy amidst perturbations in the physical and sensory environment. For each test object, the success rates depicted a strong correlation with the usage of the correct versus incorrect parametric biases, validating the model's premises about the importance of synchronous sensor-actuator state recognition.

Potential applications for this research extend into realms where low-cost, lightweight robotic implementations are prioritized or indispensable, such as in service robotics, distributed sensors in flexible environments, and adaptive manufacturing processes. The extrapolation to more complex, iterative tasks or multi-object manipulation underscores a potent avenue for future research.

This research addresses robotic vision and control's agile adaption, posing a significant contribution towards autonomous calibration processes within robotic systems, specifically those limited by structural pliability and where multi-sensor integration and real-time adaptability bear significant applications. Future work may focus on the exploratory application of this method to a wider range of robots with variable structural configurations and environmental influences to further test and refine the efficacy and robustness of the parametric bias framework introduced herein.

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