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Found 7 papers, 1 papers with code
Evaluating Multimodal Interactive Agents
Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research.
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
Open-Ended Learning Leads to Generally Capable Agents
The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.
Imitating Interactive Intelligence
These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone.
Grounded Language Learning Fast and Slow
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text
Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text
We address this issue by integrating language encoders that are pretrained on large text corpora into a situated, instruction-following agent.
