Imitating Task and Motion Planning with Visuomotor Transformers

Murtaza Dalal, Ajay Mandlekar, Caelan Reed Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox

Keywords: Imitation Learning, Task and Motion Planning, Transformers

TL;DR: We propose a novel method for training large scale imitation policies for robotic manipulation by distilling Task and Motion Planning into visuomotor Transformers.

Abstract: Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contrast, Task and Motion Planning (TAMP) can autonomously generate large-scale datasets of diverse demonstrations. 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. We present a novel imitation learning system called OPTIMUS that trains large-scale visuomotor Transformer policies by imitating a TAMP agent. We conduct a thorough study of the design decisions required to imitate TAMP and demonstrate that OPTIMUS can solve a wide variety of challenging vision-based manipulation tasks with over 70 different objects, ranging from long-horizon pick-and-place tasks, to shelf and articulated object manipulation, achieving 70 to 80% success rates. Video results and code at https://mihdalal.github.io/optimus/

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Website: https://mihdalal.github.io/optimus/

Video: https://youtu.be/2ItlsuNWi6Y