Deep Visual Foresight for Planning Robot Motion (1610.00696v2)

Abstract: A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.

• The paper demonstrates a deep video prediction model integrated with MPC to accurately predict pixel outcomes for planning robot movements.
• The method uses self-supervised learning on 50,000 pushing attempts with diverse objects to generalize to novel circumstances.
• Experimental results outperform baselines by achieving a mean pixel distance of 2.52 ± 1.06, highlighting improved planning accuracy.

Deep Visual Foresight for Planning Robot Motion

The paper "Deep Visual Foresight for Planning Robot Motion" addresses the significant problem in robotics of enabling autonomous robot learning for diverse skills and environments without extensive human guidance. The proposed approach effectively combines deep action-conditioned video prediction with model-predictive control (MPC) to facilitate self-supervised robotic learning from unlabeled video data.

Methodology and Contributions

The developed methodology leverages video prediction models within a probabilistic MPC framework to autonomously predict the visual outcomes of potential actions. This method is characterized by its minimal requirements for human intervention or detailed supervision, as it operates without a calibrated camera, 3D models, depth sensing, or a physics simulator. Instead, it directly uses raw sensory observations to predict the effects of actions, planning in the image space by moving specified pixels to desired locations.

The following are key aspects of the approach:

• Data Collection: 50,000 pushing attempts were collected using 10 robotic arms, involving hundreds of varied objects. This extensive dataset was utilized to train the video prediction model.
• Deep Predictive Model: The model is a convolutional LSTM used to predict future frames in an image sequence. It estimates the state evolution conditioned on action sequences, predicting the next image through transformations indicated by learned probabilistic flow maps.
• End-to-End Training: Training is conducted in an entirely self-supervised manner, using raw video data to learn implicit physical models of the environment, robust to new, unseen objects.

Experimental Evaluations and Numerical Results

The researchers conducted both qualitative and quantitative experiments to validate their approach. The performance was evaluated on novel objects, not included in the training dataset, to ascertain the generalization capability of the learned models. The experimental setup involved a series of nonprehensile manipulation tasks, specifically focusing on pushing objects in a controlled environment.

Quantitatively, the method demonstrated improved performance over three baselines: random action selection, end-effector movement to goal position, and continuous replanning using optical flow. The performance is substantiated by the reported mean pixel distances, showing that the visual MPC method achieves the lowest mean distance between final and goal pixel positions (2.52 ± 1.06) compared to the baselines.

Implications and Future Developments

This research showcases a scalable, autonomous approach for robot learning in unstructured environments, underlining several theoretical and practical implications:

• Practical Utility: The presented method can be particularly beneficial in settings where extensive human intervention or precise calibration is impractical. Through self-supervision, robots can continuously improve by accumulating operational experiences.
• Model Flexibility: By focusing on pixel-level objectives rather than object-specific manipulations, the approach facilitates straightforward generalization to various tasks and objects, making it versatile for practical deployment.
• Reduced Need for Simulation: The demonstrated ability to predict and plan without detailed physical simulations emphasizes the potential of data-driven models in simplifying the traditionally complex pipeline of robot control.

Future developments could explore advancements in predictive modeling to enhance the precision and reliability of the approach. Hierarchical models enabling long-term planning would be a promising direction, broadening the scope of tasks to more complex and prolonged interactions. Additionally, leveraging advances in computational hardware could facilitate higher fidelity and real-time performance, pushing the boundaries of autonomous robotic systems further into practical, real-world applications.

In conclusion, the paper contributes significantly to the field of autonomous robotic manipulation by introducing a method that efficiently combines deep learning and MPC for visual foresight, paving the way for more sophisticated and autonomous robotic systems capable of operating with minimal human intervention.