End-to-End Training of Deep Visuomotor Policies
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.
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Synopsis
Overview
- Keywords: Reinforcement Learning, Deep Learning, Visuomotor Control, Neural Networks, Policy Search
- Objective: Investigate whether joint end-to-end training of perception and control systems yields better performance than separate training.
- Hypothesis: Training the perception and control systems jointly will produce more effective visuomotor policies compared to training them independently.
Background
Preliminary Theories:
- Policy Search Methods: Techniques that allow robots to learn control policies through experience, often requiring hand-engineered components for perception and control.
- Guided Policy Search: A method that transforms policy search into supervised learning, facilitating the training of high-dimensional policies.
- Convolutional Neural Networks (CNNs): Deep learning architectures that excel in processing visual data, crucial for mapping raw image observations to control commands.
- Visual Servoing: A feedback control approach that uses visual input to guide robotic actions, typically requiring predefined feature points.
Prior Research:
- 2010: Kober et al. introduced policy search methods for robotic manipulation.
- 2014: Levine and Abbeel proposed guided policy search, demonstrating its effectiveness in learning control policies.
- 2015: Further advancements in CNNs for robotic applications highlighted the potential for end-to-end learning in complex tasks.
Methodology
Key Ideas:
- End-to-End Training: The method trains a policy that directly maps camera images to motor torques, utilizing a CNN architecture with 92,000 parameters.
- Trajectory Optimization: A model-free approach that uses full state information during training, while the final policy operates on visual observations.
- Spatial Softmax Layer: A novel layer that allows the network to learn spatial features relevant for control without losing locational information.
Experiments:
- Evaluated on tasks such as inserting blocks into a shape-sorting cube, screwing caps onto bottles, and manipulating objects with a PR2 robot.
- Metrics included success rates across training positions, novel test positions, and in the presence of visual distractors.
Implications: The design allows for efficient learning of complex policies with limited data, demonstrating the potential for real-world applications in robotic manipulation.
Findings
Outcomes:
- End-to-end trained policies significantly outperformed those trained with fixed vision layers, achieving high success rates across various tasks.
- The end-to-end approach improved generalization to novel scenarios and robustness against visual distractors.
- Policies learned to identify task-relevant features more effectively than those trained separately.
Significance: This research challenges traditional modular approaches, demonstrating that joint training can yield superior performance in visuomotor tasks.
Future Work: Suggested avenues include exploring more complex policy architectures, integrating additional sensory modalities, and addressing generalization challenges in diverse environments.
Potential Impact: Advancements in end-to-end training could revolutionize robotic control systems, enabling more adaptable and efficient robots capable of performing complex tasks in dynamic settings.