no code implementations • 9 Aug 2024 • Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel
We utilize this embedding space and the clustering it supports to self-generate pairings between trajectories in the large unpaired dataset.
no code implementations • 8 May 2024 • Yide Shentu, Philipp Wu, Aravind Rajeswaran, Pieter Abbeel
This enables LLMs to flexibly communicate goals in the task plan without being entirely constrained by language limitations.
no code implementations • 16 Oct 2023 • Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik
To tackle this, we propose a simple framework that achieves interactive task planning with language models by incorporating both high-level planning and low-level skill execution through function calling, leveraging pretrained vision models to ground the scene in language.
1 code implementation • 4 May 2023 • Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making.
1 code implementation • 9 Apr 2023 • Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
1 code implementation • 28 Jun 2022 • Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel
Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.