251 citations · 478 across the 17 of their papers we have counts for
8 papers · 1 filter
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Jonathan N. Lee, Annie Xie, Aldo Pacchiano +4
Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trai…
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
Annie Xie, Fahim Tajwar, Archit Sharma +1
A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irrev…
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie, Shagun Sodhani, Chelsea Finn +2
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods provide a way to infer the variation from on…
Lifelong Robotic Reinforcement Learning by Retaining Experiences
Annie Xie, Chelsea Finn
Multi-task learning ideally allows robots to acquire a diverse repertoire of useful skills. However, many multi-task reinforcement learning efforts assume the robot can collect dat…
Deep Reinforcement Learning amidst Lifelong Non-Stationarity
Annie Xie, James Harrison, Chelsea Finn
As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives. In contrast, typical…
Learning Predictive Models From Observation and Interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin +4
Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated…
Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie, Avi Singh, Sergey Levine +1
Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an…
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Tianhe Yu, Chelsea Finn, Annie Xie +4
Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning fr…