Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version) (2312.00592v3)

Abstract: Reinforcement learning (RL) for robot control typically requires a detailed representation of the environment state, including information about task-relevant objects not directly measurable. Keypoint detectors, such as spatial autoencoders (SAEs), are a common approach to extracting a low-dimensional representation from high-dimensional image data. SAEs aim at spatial features such as object positions, which are often useful representations in robotic RL. However, whether an SAE is actually able to track objects in the scene and thus yields a spatial state representation well suited for RL tasks has rarely been examined due to a lack of established metrics. In this paper, we propose to assess the performance of an SAE instance by measuring how well keypoints track ground truth objects in images. We present a computationally lightweight metric and use it to evaluate common baseline SAE architectures on image data from a simulated robot task. We find that common SAEs differ substantially in their spatial extraction capability. Furthermore, we validate that SAEs that perform well in our metric achieve superior performance when used in downstream RL. Thus, our metric is an effective and lightweight indicator of RL performance before executing expensive RL training. Building on these insights, we identify three key modifications of SAE architectures to improve tracking performance.

• The paper introduces a novel metric that quantifies the tracking error between SAE-obtained keypoints and ground truth positions for RL tasks.
• It evaluates multiple SAE architectures, revealing that modifications like velocity loss and trainable Gaussian std improve tracking capability.
• Empirical results show a strong link between effective keypoint tracking and RL performance, establishing the metric as a valuable pre-training tool.

Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection

In the context of reinforcement learning (RL) for robot control, a detailed representation of the environment state is paramount, particularly for tasks involving dynamic and unstructured environments where traditional sensing methods fall short. Keypoint detectors, such as spatial autoencoders (SAEs), provide a means to distill high-dimensional image data into low-dimensional, task-relevant representations. This paper by Emma Cramer et al. addresses the challenge of evaluating the effectiveness of SAEs in tracking object positions, proposing a novel metric to quantitatively assess their performance, and further explores the implications of this metric on downstream RL tasks.

The Proposed Metric

The crux of the paper lies in the introduction of a metric to evaluate the tracking performance of SAEs. The authors define this problem as assessing how well the keypoints, obtained via an SAE, represent ground truth object positions over time. The metric incorporates several considerations:

1. Affine Transformation: Keypoints may not directly correspond to ground truth positions due to consistent spatial offsets. Thus, an affine transformation ($\hat{z} = A z + b$) is used to align keypoints with ground truth positions, minimizing discrepancies.
2. Tracking Error: For each ground truth object position $x_k$ and keypoint $z_n$, a tracking error $e_{n,k}$ is calculated as the sum of Euclidean distances over all time steps. The keypoint with the minimum tracking error for each object defines the metric's accuracy.
3. Tracking Capability (TC): An object is considered correctly tracked if the tracking error is below a threshold $\mu_k$. The overall SAE performance is quantified as the percentage of correctly tracked objects.

Empirical Evaluation of SAEs

The paper evaluates common SAE architectures on a simulated robotic task (PandaPush-v3) and introduces three architectural modifications aimed at improving tracking performance:

1. Basic: A simple architecture with 16 or 32 keypoints.
2. DSAE: An architecture with a convolutional encoder and fully connected decoder.
3. KeyNet: Utilizes Gaussian kernel maps as part of a convolutional encoder-decoder structure.

The authors then propose and evaluate modifications including a velocity loss term (-vel), trainable Gaussian standard deviations (-std), and a background bias layer (-bg).

Numerical Results and Analysis

The results demonstrate significant variation in SAE performance across architectures, with the KeyNet-vel-std-bg modification showing the best tracking capabilities (mean TC of 0.986). This is further corroborated by a detailed analysis of tracking errors, which consistently fall below threshold values for this architecture. The empirical results indicate that traditional metrics like reconstruction loss are insufficient for evaluating SAE performance, highlighting the importance of the proposed tracking error and tracking capability metrics.

Implications for Reinforcement Learning

A critical aspect of the paper is linking SAE performance to RL success. Extensive experiments reveal a strong correlation: architectures with better object tracking metrics generally achieve higher RL performance. Specifically, RL agents utilizing state representations derived from well-performing SAEs like KeyNet-vel-std-bg achieve success rates comparable to those with full ground truth states. This result underscores the utility of the proposed metric as a lightweight pre-evaluation tool to select suitable SAEs before engaging in computationally intensive RL training.

Future Directions

The findings open avenues for further research in:

1. 3D Keypoint Extraction: Extending the current 2D evaluation to 3D keypoints could enhance the applicability of SAEs in more complex environments.
2. Alternative Keypoint Detectors: Exploring non-SAE keypoint extraction methods may provide additional insights and potentially superior performance.
3. Broader Application Domains: Validating the proposed metric across different RL tasks and real-world robotic applications could further establish its generalizability and practical utility.

Conclusion

This paper provides a substantial contribution to the field of robotic RL by introducing a robust metric for evaluating the spatial tracking performance of SAEs. The metric enables a nuanced understanding of SAE capabilities, directly informing the design of RL systems. The demonstrated link between the metric and RL success rates promises significant efficiency gains in RL training workflows, paving the way for more effective and resource-efficient robotic control solutions.