Continual Learning for Instruction Following from Realtime Feedback Yoav Artzi University of California, Berkeley

We propose and deploy an approach to continually train an instruction-following agent from feedback provided by users during collaborative interactions. During interaction, human users instruct an agent using natural language, and provide realtime binary feedback as they observe the agent following their instructions.