  Learning abstract state representations and knowledge is crucial for
long-horizon robot planning. We present InterPreT, an LLM-powered framework for
robots to learn symbolic predicates from language feedback of human non-experts
during embodied interaction. The learned predicates provide relational
abstractions of the environment state, facilitating the learning of symbolic
operators that capture action preconditions and effects. By compiling the
learned predicates and operators into a PDDL domain on-the-fly, InterPreT
allows effective planning toward arbitrary in-domain goals using a PDDL
planner. In both simulated and real-world robot manipulation domains, we
demonstrate that InterPreT reliably uncovers the key predicates and operators
governing the environment dynamics. Although learned from simple training
tasks, these predicates and operators exhibit strong generalization to novel
tasks with significantly higher complexity. In the most challenging
generalization setting, InterPreT attains success rates of 73% in simulation
and 40% in the real world, substantially outperforming baseline methods.
