Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms’ research and application on disassembly tasks.
skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. Apart from supporting environments that use the traditional OpenAI Gym interface, it allows loading, configuring, and operating NVIDIA Isaac Gym environments, enabling the parallel training of several agents with adjustable scopes, which may or may not share resources, in the same execution. The library's documentation can be found at https:// skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.
We introduce an instrument for a wide spectrum of experiments on gravities other than our planet's. It is based on a large Atwood machine where one of the loads is a bucket equipped with a single board computer and different sensors. The computer is able to detect the falling (or rising) and then the stabilization of the effective gravity and to trigger actuators depending on the experiment. Gravities within the range 0.4 g-1.2 g are easily achieved with acceleration noise of the order of 0.01 g. Under Martian gravity, we are able to perform experiments of approximately 1.5 s duration. The system includes features such as WiFi and a web interface with tools for the setup, monitoring, and data analysis of the experiment. We briefly show a case study in testing the performance of a model Mars rover wheel in low gravities.
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