  In this paper, we study the problem of learning a repertoire of low-level
skills from raw images that can be sequenced to complete long-horizon
visuomotor tasks. Reinforcement learning (RL) is a promising approach for
acquiring short-horizon skills autonomously. However, the focus of RL
algorithms has largely been on the success of those individual skills, more so
than learning and grounding a large repertoire of skills that can be sequenced
to complete extended multi-stage tasks. The latter demands robustness and
persistence, as errors in skills can compound over time, and may require the
robot to have a number of primitive skills in its repertoire, rather than just
one. To this end, we introduce EMBER, a model-based RL method for learning
primitive skills that are suitable for completing long-horizon visuomotor
tasks. EMBER learns and plans using a learned model, critic, and success
classifier, where the success classifier serves both as a reward function for
RL and as a grounding mechanism to continuously detect if the robot should
retry a skill when unsuccessful or under perturbations. Further, the learned
model is task-agnostic and trained using data from all skills, enabling the
robot to efficiently learn a number of distinct primitives. These visuomotor
primitive skills and their associated pre- and post-conditions can then be
directly combined with off-the-shelf symbolic planners to complete long-horizon
tasks. On a Franka Emika robot arm, we find that EMBER enables the robot to
complete three long-horizon visuomotor tasks at 85% success rate, such as
organizing an office desk, a file cabinet, and drawers, which require
sequencing up to 12 skills, involve 14 unique learned primitives, and demand
generalization to novel objects.
