Machine learning can allow robots to acquire complex skills, such as grasping and opening doors. However, learning these skills requires us to manually program reward functions that the robots then attempt to optimize. In contrast, people can understand the goal of a task just from watching someone else do it, or simply by being told what the goal is. We can do this because we draw on our own prior knowledge about the world: when we see someone cut an apple, we understand that the goal is to produce two slices, regardless of what type of apple it is, or what kind of tool is used to cut it. Similarly, if we are told to pick up the apple, we understand which object we are to grab because we can ground the word “apple” in the environment: we know what it means.

These are semantic concepts: salient events like producing two slices, and object categories denoted by words such as “apple.” Can we teach robots to understand semantic concepts, to get them to follow simple commands specified through categorical labels or user-provided examples? In this post, we discuss some of our recent work on robotic learning that combines experience that is autonomously gathered by the robot, which is plentiful but lacks human-provided labels, with human-labeled data that allows a robot to understand semantics. We will describe how robots can use their experience to understand the salient events in a human-provided demonstration, mimic human movements despite the differences between human robot bodies, and understand semantic categories, like “toy” and “pen”, to pick up objects based on user commands.

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