Deep Spatial Autoencoders for Visuomotor Learning (1509.06113v3)

Abstract: Reinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot's arm.

• The paper introduces a deep spatial autoencoder that learns object-centric feature points from visual data to automate state representation for RL.
• The approach integrates unsupervised feature extraction with a three-stage reinforcement learning process, reducing manual engineering for robot control.
• Experimental results demonstrate effective manipulation across tasks like block pushing and loop hanging with minimal training time.

Deep Spatial Autoencoders for Visuomotor Learning

The paper "Deep Spatial Autoencoders for Visuomotor Learning" presents a method for the automated acquisition of robotic manipulation skills using reinforcement learning (RL). This research addresses the challenge of state-space representation, typically a labor-intensive process, by learning it directly from camera images. The core contribution lies in employing deep spatial autoencoders to derive feature points that encapsulate task-relevant environmental configurations, such as object positions, which can then be used in conjunction with a sample-efficient RL algorithm.

Key Methods and Contributions

The authors introduce a spatial autoencoder architecture that learns a representation in the form of real-valued feature points from visual inputs. These feature points convey object-centric spatial information, making them well-suited for integration into RL frameworks that rely on local linear models. Notably, this architecture is designed to reduce the number of non-convolutional parameters, enhancing data efficiency.

The approach involves three primary stages:

1. Initialization: An RL controller is trained without visual inputs to explore the task space and gather image data. This stage uses basic state information such as joint angles and end-effector positions.
2. Feature Extraction: A deep spatial autoencoder processes the collected images to learn spatial features, focusing on object locations rather than semantic content.
3. Controller Training: A vision-based controller is developed using the augmented state representation, which now includes the learned visual features.

This methodological framework allows the researchers to demonstrate the successful application of their approach across various robotic tasks, such as block pushing, bag scooping, and loop hanging, performed by a PR2 robot. The system autonomously learns hand-eye coordination for manipulation tasks without explicit information about the objects involved.

Results and Evaluation

The experimental results depict the efficacy of the proposed method. The paper emphasizes the ability of the learned controllers to dynamically react to task-relevant visual features, achieving high success rates on multiple manipulation tasks when compared to non-vision controllers. Moreover, the learned representations allow for sample-efficient learning, with the implementation on the robot requiring as little as 10-15 minutes of interaction time.

Comparative Analysis and Implications

The paper positions its method against existing state representation techniques, noting the limitations of previous approaches, particularly those requiring extensive data or task-specific engineering. Notably, the integration of the autoencoder with a trajectory-centric RL approach presents a significant advancement in facilitating real-world applications.

The implications of this work extend beyond practical robotics by suggesting a framework where vision and control can be tightly coupled through automatic representation learning. Furthermore, this approach hints at future possibilities where robots could learn diverse manipulation tasks in various unstructured environments with minimal human intervention.

Future Directions

Potential avenues for future work include extending the framework to incorporate additional sensory modalities such as depth and haptic feedback, which could yield richer representations. Moreover, addressing tasks that require more complex exploration strategies could benefit from iteratively interleaving representation learning with control optimization, further enhancing robot autonomy and adaptability.

In summary, this research contributes significantly to the field of robot visuomotor learning by introducing a novel, efficient method for state representation learning that enables the automatic acquisition of complex manipulation skills from visual data.