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Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Stephen James, Kentaro Wada, Tristan Laidlow, Andrew J. Davison
TL;DR
Continuous actor-critic methods can be unstable and data-inefficient for sparsely rewarded, image-based robotic manipulation. C2F-ARM recursively discretises translation with coarse-to-fine Q-attention and deep Q-learning, achieving strong benchmark performance and learning five real-world task sets from three demonstrations. Its main scope limitation is that the initial voxel resolution must remain reasonably small, which becomes problematic for mobile manipulation over large environments.
Problem
Continuous actor-critic reinforcement learning methods are unstable and data-inefficient for sparsely rewarded, image-based robotic manipulation.
Method
C2F-ARM recursively applies coarse-to-fine Q-attention to voxelised scenes, discretising translation and replacing the actor-critic next-best-pose agent with deep Q-learning.
Results
C2F-ARM outperforms other methods and rapidly learns real-world tasks, including five diverse task sets from only three demonstrations.
Takeaways & Limitations
The system provides sample-efficient learning for sparsely rewarded manipulation in simulation and the real world, while supporting multiple cameras through canonical-world-frame voxelisation.
Takeaways & Limitations
The initial voxel resolution must remain reasonably small, which is manageable for fixed-table manipulation but problematic for mobile manipulation across an entire room or house.
Abstract
from arXiv · showhide
We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to 'zoom' into. When this 'zooming' behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarse-to-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations.
1. Introduction
C2F-ARM replaces unstable continuous actor-critic control with coarse-to-fine discretisation and deep Q-learning for sparsely rewarded visual manipulation. It achieves sample-efficient learning across simulation and real-world tasks, including five task sets from three demonstrations.
- Motivation: C2F-ARM targets diverse real-world and simulated manipulation tasks using few demonstrations and sparsely rewarded exploration.The motivation is to reduce imitation learning’s covariate-shift problem and reinforcement learning’s large interaction burden.
- Method: The method discretises the translation space, enabling replacement of the unstable actor-critic next-best-pose agent with deep Q-learning.Rotation and gripper actions are already bounded and straightforward to discretise; translation is the main challenge.
- Evaluation: C2F-ARM is benchmarked against robot learning methods from reinforcement learning and imitation learning.The paper evaluates the system on eight RLBench tasks and compares it with several established baselines.
- Method: Coarse-to-fine Q-attention recursively zooms into voxelised scene regions, using each selected location as the next voxel centroid.RGB and point-cloud observations from multiple cameras are processed at successive resolutions.
- Results: C2F-ARM is more sample-efficient and stable to train than other methods, while learning five diverse real-world task sets from only three demonstrations.The real-world tasks include turning on a light, pulling objects or cloth, removing a saucepan lid, and folding a towel.
2. Related Work
Prior manipulation learning commonly uses unstable and sample-inefficient actor-critic methods or task-specific discrete action spaces. This work instead presents a full 6D voxel-based reinforcement learning system intended to extend beyond top-down pick-and-place.
- Learning for manipulation: Continuous-control reinforcement learning methods such as PPO, DDPG, TD3, and SAC commonly rely on actor-critic formulations that can be sample-inefficient and unstable.These methods have been applied to tasks including cloth manipulation, lego stacking, pushing, and in-hand manipulation.
- Discrete action spaces: Existing discrete-action manipulation approaches discretise joints or planar workspaces, limiting their direct extension beyond top-down pick-and-place tasks.The paper contrasts joint-step actions and pixel-based planar actions with broader 6D manipulation.
- Contribution: C2F-ARM presents a full 6D manipulation system that can extend to a range of tasks rather than only top-down ones.The cited experiments include tasks such as stacking wine and taking an object from a shelf.
- Voxel representation: Voxel representations have been used for navigation, object selection, pose estimation, and supervised 6DoF grasp generation.C2F-ARM differs by using voxels within vision-based reinforcement learning for full 6D robot manipulation.
3. Background
The background formalises reinforcement learning and describes ARM’s Q-attention as a value-based visual focusing mechanism. Q-attention selects high-value image regions, crops observations, and is trained with a target-Q loss before an actor-critic pose stage.
- Reinforcement learning: Reinforcement learning models an agent interacting with states, actions, and rewards to maximise expected discounted return.The discount factor γ weights future rewards, and Q(s, a) represents expected return after taking action a in state s.
- Deep Q-learning: Deep Q-learning approximates the value function with a convolutional network trained from replay-buffer minibatches against a target network.The target network is periodically copied from the online network and is not directly optimised.
- ARM: ARM introduced Q-attention, keypoint detection, demonstration augmentation, and a high-level next-best-pose action space for vision-based manipulation.Q-attention is the concept most directly reused to discretise the translation space in this paper.
- Q-attention: Given RGB, point-cloud, and proprioceptive observations, Q-attention selects the highest-value 2D pixel location as the next area of interest.The selected location is obtained by a two-dimensional argmax over Q-values.
- Q-attention: The selected pixels crop the RGB image and organised point cloud, reducing the next stage’s input before an actor-critic next-best-pose agent processes it.Q-attention parameters are optimised by stochastic gradient descent using a target-Q objective with Q regularisation.
- Demonstration processing: Keyframe discovery and demonstration augmentation populate the replay buffer with informative intermediate-to-keyframe transitions for training Q-attention.This increases the amount of initial demonstration data available to the learner.
4. Method
C2F-ARM discretises continuous 6D manipulation poses by recursively refining voxelised scene regions with Q-attention, then uses a control agent to execute the predicted pose.
- System overview: C2F-ARM splits manipulation into coarse-to-fine Q-attention and low-level control phases.The first phase predicts a next-best pose; the second executes it through motion planning or learned control.
- Training: Demonstrations initialise the replay buffer through keyframe selection and augmentation before online interaction and gradient updates.Transitions include the observation, action, reward, next observation, and extracted coordinates.
- Coarse-to-fine Q-attention: Each Q-attention depth voxelises the scene and selects the highest-valued voxel as the centre for the next, finer voxelisation.This recursively focuses computation on a specific scene region while retaining a low memory footprint.
- Inputs and rewards: The system uses voxelised observations containing coordinates, scene features, and occupancy information as inputs to the Q-attention networks.The observation combines RGB images, organised point clouds, and proprioceptive data under sparse rewards.
- Coarse-to-fine Q-attention: At the final depth, the selected voxel centre provides the continuous translation component of the next-best pose.The extracted coordinates become the next grid centre at intermediate depths and the translation representation at the final depth.
- Pose prediction: Rotation is discretised in 5-degree increments and gripper state is represented as open or closed using a simpler prediction branch.These spaces are smaller than translation and are predicted from the final Q-attention depth.
5. Results
C2F-ARM is evaluated on sparse-reward RLBench tasks, multi-camera settings, robustness variations, and real-world manipulation. Across these experiments, it shows strong performance with limited demonstrations, while performance depends on camera information and voxel-resolution choices.
- Simulation comparison: C2F-ARM outperforms ARM by a large margin or reaches the same performance in substantially fewer environment steps.The comparison uses eight RLBench tasks and gives C2F-ARM only 10 demonstrations versus 100 for the baselines.
- Simulation comparison: Conventional actor-critic baselines perform particularly poorly on challenging vision-based, sparsely-rewarded tasks.The authors note that successful training may require privileged simulation-only abilities such as reward shaping or resets to demonstrations.
- Practical considerations: Inference is twice as slow because C2F-ARM uses 3D rather than 2D convolutions, but this time is negligible relative to arm navigation.The method also required little to no hyperparameter tuning, whereas the baselines required substantial tuning.
- Multi-camera evaluation: Using wrist, left-shoulder, and right-shoulder cameras enables C2F-ARM to perform well on tasks that cannot be done with only the front-facing camera.The cameras are fused into a single voxel grid, so adding cameras does not require modifying the rest of the system.
- Robustness and ablations: C2F-ARM remains robust across coarse-to-fine depths and voxel grid sizes, but performance deteriorates when both are jointly reduced.Larger voxel grids improve performance but increase memory use; increasing coarse-to-fine depth offers gains with only a small memory-footprint increase.
- Real-world evaluation: C2F-ARM learns five real-world tasks from scratch using 3 demonstrations, with approximate training times ranging from 6 to 42 minutes.The experiments use a Franka Emika Panda and a single RGB-D RealSense camera, stopping after four consecutive successes per task.
6. Discussion and Conclusion
C2F-ARM replaces ARM’s unstable actor-critic next-best-pose agent with recursively applied coarse-to-fine Q-attention and a discrete control agent. The resulting system supports sample-efficient manipulation, including multi-camera settings, while retaining several unresolved limitations for larger-scale or dynamically complex environments.
- Conclusion: C2F-ARM is described as sample-efficient, outperforming other methods and rapidly learning real-world tasks.The paper attributes this result to replacing unstable actor-critic methods with more stable deep Q-learning enabled by translation discretisation.
- Discussion: C2F-ARM recursively applies 3D Q-attention to discretise the large translation space, removing ARM’s actor-critic next-best-pose agent.Its two stages are coarse-to-fine Q-attention and a control agent.
- Discussion: Canonical-world-frame voxelisation lets C2F-ARM support multiple cameras or a single moving camera without the camera-specific problems affecting ARM.ARM could behave undefinedly when observations lacked interesting pixels and could mis-size crops for moving cameras.
- Limitations: The voxel representation currently stores only raw RGB and point-cloud data, while learned pixel features are proposed as a future improvement.The authors expect richer voxel values could help at small resolutions or with few coarse-to-fine Q-attention layers.
- Limitations: Initial voxel resolution must remain reasonably small, which is manageable for fixed-table manipulation but problematic for mobile manipulation across an entire room or house.The authors identify this as an area for future investigation.
- Limitations: The control agent will require improvement for dynamic environments and complex contact dynamics such as moving obstacles or peg-in-hole tasks.The current system uses path planning and online trajectory generation.