Language grounding in reinforcement learning can be realized with the PDDL syntax. A domain is described with motion primitives like grasp, stand-up, walkto and the primitives are used to abstract from lowlevel servo actions. For the untrained eye, the concept of language grounding seems to be a promising method to handle complex domains with reinforcement learning. The state-space gets smaller and the agent's behavior is transparent because the program is using the same words like a human will do.
But, it's possible to describe grounding as a dead end. The real benefit of a PDDL solver is not, that English verbs are used to describe the actions, but the dominant feature is that a PDDL file can predict future outcomes of a system. For example, the action “walkto” is executed, and the robot is in a new position on the map. It's possible to change the action name to a random string, which means that the domain is no longer grounded. But, the post-condition in the pddl file remains the same. And the solver is able to predict what will happen after the un-named function gets executed. So the advantage isn't the name of actions, but it's the prediction engine which knows what the result of an action is.
Model based language processing A typical example for grounded language is a textadventure. The user gets feedback from the system only with textual words and he has to enter commands in English. As default, a textadventure provides a forward model. After entering a command sequence the game will provide the future state. The only problem is, that for most domains no textadventure is available but only a textcorpus. It make sense to convert an existing textcorpus into a game, which means to take the textual sequence as input and generate a playable forward simulation as output. In such a simulation it's possible to take trial actions and answer what-if problems. The system uses the database to retrieve sequence of steps to reach a predefined goal.