Exploring Cross-Embodiment to Reduce Costs in Robot Model Training
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- leaderobot.com
The embodied intelligence industry is facing significant waste due to the need for retraining models whenever a new robot is introduced. As the number of robot types could reach tens of thousands, the industry is exploring concepts like 'one brain, multiple forms' to enable shared intelligence among different robots.
This approach is crucial as developing separate brains for each robot type would lead to exorbitant costs and hinder the accumulation of shared knowledge. Current research indicates that while humanoid robots are not yet fully mature, the future may involve multiple robot configurations coexisting, each tailored for specific tasks.
As the industry progresses, finding commonalities among different robot configurations could allow for the transfer of learned capabilities, thereby reducing the need for extensive retraining. The focus on cross-embodiment is becoming increasingly important as it may lead to more efficient use of resources and improved scalability in robot development.
Original report
Can We Avoid the Costly Waste of Retraining Robots for Every New Model?
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