Selected Research Projects
SMART: SELF-SUPERVISED MULTI-TASK PRETRAINING WITH CONTROL TRANSFORMERS
In this work, we formulate a general pretraining-finetuning pipeline for sequential decision making, under which we propose a generic pretraining framework Self-supervised Multi-task pretrAining with contRol Transformer (SMART). By systematically investigating pretraining regimes, we carefully design a Control Transformer (CT) coupled with a novel control-centric pretraining objective in a self-supervised manner.
LATTE: LAnguage Trajectory TransformEr
This work proposes a flexible language-based framework that allows a user to modify generic robotic trajectories. Our method leverages pre-trained language models (BERT and CLIP) to encode the user’s intent and target objects directly from a free-form text input and scene images, fuses geometrical features generated by a transformer encoder network, and finally outputs trajectories using a transformer decoder, without the need of priors related to the task or robot information.