Source-linked AI summary
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
Stephen James, Andrew J. Davison, Edward Johns
TL;DR
The paper addresses limited robustness and long-horizon capability in end-to-end robot control trained without real images. It generates demonstrations in simulation, trains a reactive image-to-velocity controller with domain randomisation, and transfers it to a real multi-stage tidying task. The resulting controller performs the task across varied and dynamic real-world environments.
Problem
End-to-end robot controllers need to transfer from scalable simulation training to real-world manipulation while handling long-horizon multi-stage tasks.
Method
The method trains a reactive neural controller on simulated inverse-kinematics demonstrations, using domain randomisation and auxiliary position outputs for simulation-to-real transfer.
Results
The controller transfers to the real world and performs the multi-stage cube-to-basket task across variations in objects, camera, lighting, distractors, and moving objects.
Takeaways & Limitations
The demonstrated approach supports end-to-end execution of a long-horizon multi-stage manipulation task without real training images.
Abstract
from arXiv · showhide
End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, we show how two simple techniques can lead to end-to-end (image to velocity) execution of a multi-stage task, which is analogous to a simple tidying routine, without having seen a single real image. This involves locating, reaching for, and grasping a cube, then locating a basket and dropping the cube inside. To achieve this, robot trajectories are computed in a simulator, to collect a series of control velocities which accomplish the task. Then, a CNN is trained to map observed images to velocities, using domain randomisation to enable generalisation to real world images. Results show that we are able to successfully accomplish the task in the real world with the ability to generalise to novel environments, including those with dynamic lighting conditions, distractor objects, and moving objects, including the basket itself. We believe our approach to be simple, highly scalable, and capable of learning long-horizon tasks that have until now not been shown with the state-of-the-art in end-to-end robot control.
1 Introduction
The paper targets robust simulation-to-real transfer for end-to-end visuomotor control, demonstrating a multi-stage tidying task without real training images. The final model handles varied positions and challenging scene conditions in the real world.
- Motivation: Simulation-trained end-to-end controllers are attractive for scalable data collection but require robust transfer to real images.The paper contrasts this goal with traditional pipelines and their error propagation.
- Contribution: The demonstrated task locates, reaches for, grasps, and deposits a cube in a basket through continuous visuomotor control.
- Contribution: The controller transfers to the real world without seeing a single real image by combining simulated demonstrations with domain randomisation.
- Results: The final model handles variations in cube, basket, camera, and initial joint-angle positions while remaining robust to distractors, lighting changes, scene changes, and moving objects.
2 Related Work
Related work spans reinforcement learning, guided policy search, large-scale robot data collection, and simulation-trained controllers. These approaches motivate simulation-to-real transfer while highlighting challenges in scalability, sample efficiency, and long-horizon structured tasks.
- Reinforcement learning: Deep reinforcement learning has advanced simulated and real-world robotic control, but remains challenged by sample inefficiency, slow convergence, and hyperparameter sensitivity.
- Guided policy search: Guided policy search succeeds in manipulation but often relies on human involvement or additional real-world training, limiting scalability or direct transfer.
- Large-scale data collection: Multi-robot data collection can produce large grasp datasets, yet its scalability is questioned because of robot purchase costs and complex long-horizon data requirements.
- Simulation-to-real transfer: Other simulation-to-real methods learn grasp scoring or swing behaviours, but do not establish the same end-to-end structured multi-stage manipulation setting.
3 Approach
The approach generates multi-stage demonstrations in simulation, trains a reactive network to map visual and proprioceptive inputs to control outputs, and uses domain randomisation for real-world transfer. Auxiliary predictions and perturbed training conditions support robust execution.
- 3.1 Data Collection: Simulation generates five-stage episodes from inverse-kinematics Cartesian paths, recording images, joint angles, motor velocities, gripper actions, and object positions.Episodes randomise cube and basket placement and use V-REP for data collection.
- 3.1 Data Collection: The task sequence resets the arm, grasps and lifts the cube, moves it above the basket, opens the gripper, and verifies successful placement.Episodes lacking feasible linear paths because of obstacles are discarded; obstacle avoidance is not considered.
- 3.1 Data Collection: Domain randomisation varies scene colours, camera and object positions, lighting-related factors, arm-base height, and starting joint angles to bridge simulation and reality.Perturbing initial joint angles helps robustness to compounding execution errors.
- 3.2 Network Architecture: The reactive network processes image sequences and joint angles through convolutional layers and an LSTM before predicting motor velocities and gripper actions.The network also predicts cube and gripper positions as auxiliary outputs that aid learning but are unused during testing.
- 3.2 Network Architecture: The total loss combines velocity, gripper-action, gripper-position, and cube-position losses: L_Total = L_V + L_G + L_GP + L_CP.The loss terms are weighted equally during training.
4 Experiments
Experiments evaluate dataset size, robustness to novel environments, domain-randomisation choices, and architectural components using simulation and real-world trials. The controller generalises across several disturbances, while moving cameras, lighting, object changes, and omitted recurrent or auxiliary components expose important performance boundaries.
- Experimental Setup: Experiments vary dataset size, testing environments, domain randomisation, auxiliary outputs, and joint-angle inputs.Evaluation uses 32 trials, with success categories including cube vicinity, cube grasped, and full task.
- Robustness to New Environments: A person, moving basket, and unseen table do not prevent the robot from grasping the cube and redirecting to the basket’s new location.The training data contained none of those conditions.
- Altering Dataset Size: 200,000 images perform well in simulation, but approximately four times more data is needed for comparable real-world performance; both reach 100% at 1 million images.The comparison concerns the task without distractors.
- Robustness to New Environments: Under a moving camera, task success remains 81% and 75%, while bright moving light yields 84% cube-vicinity success but only 56% grasp success.The camera was moved within a 2-inch height range, and the spotlight moved as the robot moved.
- Robustness to New Environments: Replacing the cube with a half-length object preserves 89% vicinity success but reduces grasp success to 41%.Novel objects such as a stapler or wallet occasionally support successful completion.
- Ablation Study: Without domain randomisation, the baseline cannot complete the overall task despite reaching well; distractors also cause a distractor-free-trained controller to perform poorly.The baseline often drives the gripper into the table, while the distractor-tested controller often heads directly toward the basket.
- Ablation Study: Without camera motion during training, the full task cannot be completed, and without shadows the controller can confuse object and arm shadows with the cube.Target reaching remains unaffected, but grasping is impaired.
- Ablation Study: Removing the LSTM causes full-task failure, auxiliary outputs improve performance, and joint angles help maintain the trained gripper orientation.Without joint angles, the arm often reaches the cube vicinity but fails to preserve the orientation needed for grasping.
5 Conclusions
The paper demonstrates simulation-to-real transfer for an end-to-end controller on a long-horizon, multi-stage tidying task. It reports promising real-world results while identifying limits for tasks that cannot be staged easily or require more complex grasps.
- 5 Conclusions: The method maps images and joint angles directly to motor velocities for locating, grasping, and depositing a cube in a basket.The demonstrated sequence is a long-horizon, multi-stage tidying task.
- 5 Conclusions: The authors expect the method to extend to other staged tasks involving rigid objects but not to tasks that resist staging or require complex grasps.They cite tidying other rigid objects, stacking a dishwasher, and retrieving shelf items as possible applications.