Spatial-Language Attention Policies for Efficient Robot Learning

We investigate how to build and train spatial representations for robot decision making with Transformers. In particular, for robots to operate in a range of environments, we must be able to quickly train or fine-tune robot sensorimotor policies that are robust to clutter, data efficient, and generalize well to different circumstances. As a solution, we propose Spatial Language Attention Policies (SLAP). SLAP uses three-dimensional tokens as the input representation to train a single multi-task, language-conditioned action prediction policy. Our method shows 80% success rate in the real world across eight tasks with a single model, and a 47.5% success rate when unseen clutter and unseen object configurations are introduced, even with only a handful of examples per task. This represents an improvement of 30% over prior work (20% given unseen distractors and configurations).