Affordance Discovery using Simulated Exploration

Allowing robots to understand their world in terms of affordances allows for generalization, learning, and complex planning, while also being intuitive for humans to understand. In recent work, affordances are often learned with hand-coded robot actions, which can limit or bias the model. Real-world training has also been used to learn affordances and manipulation models, but is timeconsuming and unsafe for the robot and its environment.

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