End-to-End Training of Deep Visuomotor Policies (1504.00702v5)

Abstract: Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

• The paper presents a novel framework that integrates trajectory optimization with supervised learning to directly map raw image data to motor torques.
• It introduces a specialized CNN architecture with spatial softmax and point transformations to enhance spatial reasoning and reduce overfitting.
• Empirical evaluations on tasks like screwing bottle caps and stacking blocks show higher success rates compared to traditional baselines.

End-to-End Training of Deep Visuomotor Policies

The paper "End-to-End Training of Deep Visuomotor Policies" by Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel, addresses a pivotal question in the automation of robotic control using learning methods: Can we achieve superior sensorimotor control policies by training perception and control systems jointly end-to-end instead of separately?

The authors introduce a novel methodology that enables robots to learn policies directly mapping raw image observations to motor torques using deep convolutional neural networks (CNNs). Their method employs a guided policy search (GPS) algorithm, strategically transforming the policy search problem into a supervised learning task. Supervision is provided via trajectory-centric reinforcement learning. The work presented demonstrates substantial empirical success on various real-world manipulation tasks requiring tight integration of vision and control, such as screwing a cap onto a bottle or manipulating objects to specified positions.

Methodology

The backbone of the proposed approach lies in the integration of GPS with deep learning. The GPS algorithm alternates between trajectory optimization and supervised training phases:

1. Trajectory Optimization: Linear-Gaussian controllers are optimized using a trajectory-centric reinforcement learning method. This phase uses full state information to generate trajectories.
2. Supervised Learning: Neural network policies are trained to predict actions from observations (i.e., camera images) collected along these trajectories, facilitating end-to-end learning of visuomotor policies.

To mitigate the high sample complexity often encountered in reinforcement learning, the authors incorporated a Bregman ADMM (BADMM) based approach to formalize GPS. This framework facilitated convergence to local optima, efficiently integrating the dynamics fitting procedure and reducing the number of required iterations drastically.

A crucial component of their method is the novel CNN architecture designed specifically for robotic control, which includes spatial softmax and features point transformations to improve spatial reasoning and prevent overfitting. This deep architecture captures visual nuances essential for high-precision manipulation tasks.

Strong Numerical Results

Empirical evaluations highlighted substantial improvements over baseline methods:

1. Simulation Comparisons: The proposed GPS outperformed several existing methods such as REPS, CEM, and PILCO across tasks involving peg insertion, octopus arm control, and locomotion.
2. Real-World Robot Tasks: Learning policies for tasks like stacking blocks, screwing bottle caps, and placing a coat hanger showed high consistency and generalization to unseen configurations. For instance, the visuomotor policies for manipulation tasks on a PR2 robot achieved high success rates even in novel test conditions and in the presence of visual distractors.

Implications

Practical Implications: The ability to train visuomotor policies directly translates to greater flexibility and robustness in robotic systems. This capability can significantly reduce the engineering burden in deploying robotic solutions in variable environments (e.g., industrial automation where precise and adaptable handling of objects is required).

Theoretical Implications: The work underscores the potential of end-to-end learning approaches over modular pipelines. By leveraging deep learning frameworks, it delineates a path forward in handling high-dimensional policy representations, often infeasible with traditional RL techniques.

Speculation on Future Developments

Future research could explore augmenting the robustness of these policies against diverse visual disturbances. Integrating semi-supervised learning approaches or synthetic data augmentation might further robustify and generalize the learned policies across varied real-world scenarios. Another avenue involves extending these methods across multiple sensory modalities, potentially advancing the fusion of haptic, auditory, and visual inputs.

Additionally, improvements in computational efficiency and sample complexity could enable the application of these techniques in broader real-time control environments, opening new frontiers in autonomous systems.

In conclusion, the paper presents a significant leap in utilizing deep reinforcement learning for end-to-end visuomotor policy training. By demonstrating robust performance in real-world tasks, it paves the way for future advancements in autonomous robotic control.