no code implementations • 19 Feb 2024 • Naman Shah, Jayesh Nagpal, Pulkit Verma, Siddharth Srivastava
Empirical results in deterministic settings show that powerful abstract representations can be learned from just a handful of robot trajectories; the learned relational representations include but go beyond classical, intuitive notions of high-level actions; and that the learned models allow planning algorithms to scale to tasks that were previously beyond the scope of planning without hand-crafted abstractions.