  We study the problem of learning a range of vision-based manipulation tasks
from a large offline dataset of robot interaction. In order to accomplish this,
humans need easy and effective ways of specifying tasks to the robot. Goal
images are one popular form of task specification, as they are already grounded
in the robot's observation space. However, goal images also have a number of
drawbacks: they are inconvenient for humans to provide, they can over-specify
the desired behavior leading to a sparse reward signal, or under-specify task
information in the case of non-goal reaching tasks. Natural language provides a
convenient and flexible alternative for task specification, but comes with the
challenge of grounding language in the robot's observation space. To scalably
learn this grounding we propose to leverage offline robot datasets (including
highly sub-optimal, autonomously collected data) with crowd-sourced natural
language labels. With this data, we learn a simple classifier which predicts if
a change in state completes a language instruction. This provides a
language-conditioned reward function that can then be used for offline
multi-task RL. In our experiments, we find that on language-conditioned
manipulation tasks our approach outperforms both goal-image specifications and
language conditioned imitation techniques by more than 25%, and is able to
perform visuomotor tasks from natural language, such as "open the right drawer"
and "move the stapler", on a Franka Emika Panda robot.
