Transformers are Adaptable Task Planners
This work addresses the need for adaptable task planning in home robotics, though it is incremental as it builds on existing transformer and demonstration-based methods.
The paper tackles the problem of enabling home robots to adapt to user-specific preferences in task planning by proposing a Transformer Task Planner (TTP) that learns from demonstrations using object attributes, achieving generalization to unseen preferences with a single demonstration in simulated and real-world dish rearrangement tasks.
Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-day tasks and generalize to user's preferences. To this end, we propose a Transformer Task Planner(TTP) that learns high-level actions from demonstrations by leveraging object attribute-based representations. TTP can be pre-trained on multiple preferences and shows generalization to unseen preferences using a single demonstration as a prompt in a simulated dishwasher loading task. Further, we demonstrate real-world dish rearrangement using TTP with a Franka Panda robotic arm, prompted using a single human demonstration.