Robotic systems have come a long way from their early days of being stiff machines to the more advanced soft, humanoid, and animal-inspired robots that we see today. Among the various types of robots, legged robots, especially quadrupeds, have shown great promise in performing tasks at ground level, such as exploration and object transportation. However, most legged robots face limitations in interacting with objects and humans in their environment, often requiring additional components like robotic arms or grippers.
A team of researchers at ETH Zurich has recently introduced a novel reinforcement learning-based model that could potentially revolutionize the way four-legged robots interact with their surroundings. This model aims to enable robots to tackle more complex tasks without the need for bulky manipulators or arms. By training the robot to bring its foot to a desired position through simulations, the researchers were able to enhance the robot’s skills and make it more adaptable to real-world uncertainties.
The experiments conducted by the researchers showed significant improvement in the robot’s ability to manipulate objects. The robot was able to perform tasks such as opening a fridge door, carrying objects, pressing a button, moving obstacles, and collecting rocks, all without the need for additional tools or arms. The model teaches the robot to utilize its entire body when necessary, showcasing its versatility and adaptability in various scenarios.
The researchers foresee a wide range of applications for this new computational model in the near future. Once perfected and validated in fully automated settings, this model could expand the capabilities of legged robots in real-world scenarios. For example, robots could be used to conduct inspections in warehouses or infrastructure, pushing buttons, moving levers, and opening doors autonomously. The possibilities are endless, and the researchers are actively working towards fine-tuning the model for additional tasks like object grasping and opening different types of doors.
The advancements in object manipulation skills for legged robots are paving the way for a new era in robotic technology. The new reinforcement learning-based model developed by the researchers at ETH Zurich shows great promise in expanding the capabilities of quadruped robots without the need for additional hardware. With further improvements and automation, legged robots could soon be performing a wide range of complex tasks independently, opening up endless possibilities for their use in various industries.
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