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We propose Learned Path Ranking (LPR), a method that accepts an end-effectorgoal pose, and learns to rank a set of goal-reaching paths generated from anarray of path generating methods, including: path planning, Bezier curvesampling, and a learned policy. The core idea being that each of the pathgeneration modules will be useful in different tasks, or at different stages ina task. When LPR is added as an extension to C2F-ARM, our new system,C2F-ARM+LPR, retains the sample efficiency of its predecessor, while also beingable to accomplish a larger set of tasks; in particular, tasks that requirevery specific motions (e.g. opening toilet seat) that need to be inferred fromboth demonstrations and exploration data. In addition to benchmarking ourapproach across 16 RLBench tasks, we also learn real-world tasks, tabula rasa,in 10-15 minutes, with only 3 demonstrations.

Coarse-to-Fine Q-attention with Learned Path Ranking