no code implementations • 2 May 2024 • Ryan Hoque, Ajay Mandlekar, Caelan Garrett, Ken Goldberg, Dieter Fox
Imitation learning is a promising paradigm for training robot control policies, but these policies can suffer from distribution shift, where the conditions at evaluation time differ from those in the training data.
no code implementations • 2 Nov 2023 • Shuo Cheng, Caelan Garrett, Ajay Mandlekar, Danfei Xu
Developing intelligent robots for complex manipulation tasks in household and factory settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the need to generalize across a wide variety of object shapes and scene layouts.
no code implementations • 24 Oct 2023 • Ajay Mandlekar, Caelan Garrett, Danfei Xu, Dieter Fox
Finally, we collected 2. 1K demos with HITL-TAMP across 12 contact-rich, long-horizon tasks and show that the system often produces near-perfect agents.
no code implementations • 25 May 2023 • Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox
In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation.
no code implementations • 4 Mar 2022 • Jingkai Chen, Jiaoyang Li, Yijiang Huang, Caelan Garrett, Dawei Sun, Chuchu Fan, Andreas Hofmann, Caitlin Mueller, Sven Koenig, Brian C. Williams
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.