  Leveraging sensing modalities across diverse spatial and temporal resolutions
can improve performance of robotic manipulation tasks. Multi-spatial resolution
sensing provides hierarchical information captured at different spatial scales
and enables both coarse and precise motions. Simultaneously multi-temporal
resolution sensing enables the agent to exhibit high reactivity and real-time
control. In this work, we propose a framework, MResT (Multi-Resolution
Transformer), for learning generalizable language-conditioned multi-task
policies that utilize sensing at different spatial and temporal resolutions
using networks of varying capacities to effectively perform real time control
of precise and reactive tasks. We leverage off-the-shelf pretrained
vision-language models to operate on low-frequency global features along with
small non-pretrained models to adapt to high frequency local feedback. Through
extensive experiments in 3 domains (coarse, precise and dynamic manipulation
tasks), we show that our approach significantly improves (2X on average) over
recent multi-task baselines. Further, our approach generalizes well to visual
and geometric variations in target objects and to varying interaction forces.
