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clear.csail.mit.edu · 560 words · saved by 1 readers

As autonomous agents become increasingly woven into the fabric of society—from self-driving cars to personal robot manipulators to AI assistants—our lab aims to ensure their seamless interaction with people. However, integrating these systems into human-centered environments in a way that aligns with human expectations is a formidable challenge. Specifying human objectives to robots is difficult because these objectives are complex, context-dependent, and inherently subjective. Without the right objectives, autonomous systems may exhibit unexpected or even dangerous behaviors. Learning these objectives (for instance, as reward functions) has emerged as a popular alternative to manual specification, but it comes with its own set of difficulties: 1) getting the right data to supervise the learning is hard because humans are imperfect, not infinitely queryable, and have unique and changing preferences; 2) the representations we choose to mathematically express human objectives may themsel

Research CLEAR Interaction @ MIT --> Research Publications People Join As autonomous agents become increasingly woven into the fabric of society—from self-driving cars to personal robot manipulators to AI assistants—our lab aims to ensure their seamless interaction with people. However, integrating these systems into human-centered environments in a way that aligns with human expectations is a formidable challenge. Specifying human objectives to robots is difficult because these objectives are complex, context-dependent, and inherently subjective. Without the right objectives, autonomous syste

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