2 code implementations • 5 Feb 2018 • Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
no code implementations • 30 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.
1 code implementation • 3 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.
no code implementations • 11 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.
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.
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.
no code implementations • 12 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.
no code implementations • 19 Sep 2021 • Annie Xie, Chelsea Finn
Multi-task learning ideally allows robots to acquire a diverse repertoire of useful skills.
no code implementations • 5 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.
no code implementations • 14 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.
1 code implementation • 19 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.
no code implementations • 7 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.
no code implementations • 12 Feb 2024 • Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter
In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e. g., candidate robot actions, localizations, or trajectories).
no code implementations • 8 Mar 2024 • Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn, Dorsa Sadigh
Recent works on large-scale robotic data collection typically vary a wide range of environmental factors during data collection, such as object types and table textures.