Search Results for author: Annie Xie

Found 13 papers, 3 papers with code

Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation

no code implementations7 Jul 2023 Annie Xie, Lisa Lee, Ted Xiao, Chelsea Finn

Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors.

Imitation Learning

Supervised Pretraining Can Learn In-Context Reinforcement Learning

no code implementations26 Jun 2023 Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so.

Decision Making reinforcement-learning +1

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

1 code implementation19 Oct 2022 Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn

A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world.

Continuous Control reinforcement-learning +1

Robust Policy Learning over Multiple Uncertainty Sets

no code implementations14 Feb 2022 Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang

Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments.

Reinforcement Learning (RL)

Influencing Towards Stable Multi-Agent Interactions

no code implementations5 Oct 2021 Woodrow Z. Wang, Andy Shih, Annie Xie, Dorsa Sadigh

Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent.

Autonomous Driving

Learning Latent Representations to Influence Multi-Agent Interaction

no code implementations12 Nov 2020 Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, Dorsa Sadigh

We propose a reinforcement learning-based framework for learning latent representations of an agent's policy, where the ego agent identifies the relationship between its behavior and the other agent's future strategy.

Deep Reinforcement Learning amidst Lifelong Non-Stationarity

no code implementations ICML Workshop LifelongML 2020 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.

reinforcement-learning Reinforcement Learning (RL)

Learning Predictive Models From Observation and Interaction

no code implementations ECCV 2020 Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn

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 sequences of actions to bring about desired outcomes.

Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight

no code implementations11 Apr 2019 Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn

Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects.

Imitation Learning Self-Supervised Learning

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

1 code implementation3 Dec 2018 Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, Sergey Levine

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains.

reinforcement-learning Reinforcement Learning (RL)

Few-Shot Goal Inference for Visuomotor Learning and Planning

no code implementations30 Sep 2018 Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn

To that end, we formulate the few-shot objective learning problem, where the goal is to learn a task objective from only a few example images of successful end states for that task.

reinforcement-learning Reinforcement Learning (RL) +1

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