mlsanityData to 7 Oct 2026

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

arXiv 2204.01571 ↗First posted 4 Apr 2022cs.RO cs.AI cs.CVcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

16 of 24 references.

References · 16

  1. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  2. 2019RLBench: The Robot Learning Benchmark & Learning Environmentcited by 669
  3. 2017Asymmetric Actor Critic for Image-Based Robot Learningcited by 202
  4. 2018Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networkscited by 171
  5. 2016Value Iteration Networkscited by 158
  6. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
  7. 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 120
  8. 2021Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulationcited by 85
  9. 2018Motion Planning Networkscited by 78
  10. 2018Composable Deep Reinforcement Learning for Robotic Manipulationcited by 70
  11. 2017PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planningcited by 64
  12. 2020Object Rearrangement Using Learned Implicit Collision Functionscited by 43
4 more
  1. 2019Differentiable Gaussian Process Motion Planningcited by 27
  2. 2019Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitationcited by 23
  3. 20163D Simulation for Robot Arm Control with Deep Q-Learningcited by 19
  4. 2022Bingham Policy Parameterization for 3D Rotations in Reinforcement Learningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 1; 2024: 3; 2025: 1; 2026: 3.

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  1. 2022Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulationcited by 545
  2. 2022Masked World Models for Visual Controlcited by 169
  3. 2022Instruction-driven history-aware policies for robotic manipulationscited by 93
  4. 2024Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulationcited by 92
  5. 2023Multi-View Masked World Models for Visual Robotic Manipulationcited by 78
  6. 2024Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulationcited by 45
  7. 2024BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmarkcited by 30
  8. 2022On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learningcited by 18
  9. 2025Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulationcited by 16
  10. 2022Coarse-to-fine Q-attention with Tree Expansioncited by 9
  11. 2026KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policiescited by 5
  12. 2026Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulationcited by 1
1 more
  1. 2026SkiP: When to Skip and When to Refine for Efficient Robot Manipulationcited by 0