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Coarse-to-Fine Q-attention with Learned Path Ranking
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We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating methods, including: path planning, Bezier curve sampling, and a learned policy. The core idea being that each of the path generation modules will be useful in different tasks, or at different stages in a 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 being able to accomplish a larger set of tasks; in particular, tasks that require very specific motions (e.g. opening toilet seat) that need to be inferred from both demonstrations and exploration data. In addition to benchmarking our approach across 16 RLBench tasks, we also learn real-world tasks, tabula rasa, in 10-15 minutes, with only 3 demonstrations.
Forward citations
Cited by 3 Pith papers
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Lift3D uses task-aware depth reconstruction and mapped 2D positional embeddings to let pretrained 2D vision transformers act as 3D point-cloud manipulation policies, beating prior methods on average.
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Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation
Lift3D-VLA integrates 3D point cloud encoding and temporal action modeling into Vision-Language-Action models, achieving higher success rates on simulated and real-world robotic manipulation tasks.
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