Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulation (2407.07868v2)

Abstract: Generalising vision-based manipulation policies to novel environments remains a challenging area with limited exploration. Current practices involve collecting data in one location, training imitation learning or reinforcement learning policies with this data, and deploying the policy in the same location. However, this approach lacks scalability as it necessitates data collection in multiple locations for each task. This paper proposes a novel approach where data is collected in a location predominantly featuring green screens. We introduce Green-screen Augmentation (GreenAug), employing a chroma key algorithm to overlay background textures onto a green screen. Through extensive real-world empirical studies with over 850 training demonstrations and 8.2k evaluation episodes, we demonstrate that GreenAug surpasses no augmentation, standard computer vision augmentation, and prior generative augmentation methods in performance. While no algorithmic novelties are claimed, our paper advocates for a fundamental shift in data collection practices. We propose that real-world demonstrations in future research should utilise green screens, followed by the application of GreenAug. We believe GreenAug unlocks policy generalisation to visually distinct novel locations, addressing the current scene generalisation limitations in robot learning.

• The paper introduces green screen augmentation to enhance scene generalisation in robotic manipulation, achieving up to a 65% improvement over baseline methods.
• It leverages chroma key technology to replace static backgrounds with diverse textures, effectively overcoming the limitations of fixed training environments.
• Evaluations across eight manipulation tasks reveal that random texture augmentation delivers optimal performance, highlighting texture variability as key to robust policy learning.

Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulation

In the paper titled "Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulation," the authors propose a novel approach to enhance the generalisation capabilities of robotic manipulation tasks by leveraging green screen technology. This approach, termed Green Screen Augmentation (), addresses the pervasive issue of scene generalisation, a notable challenge in the domain of vision-based robotic learning.

Problem Context and Current Solutions

Robust generalisation of robot learning policies to novel environments remains limited due to the predominantly stationary nature of training and deployment environments. Traditionally, data is collected in a single location, and policies for imitation learning or reinforcement learning are trained on this data before being tested in the same environment. Such a method lacks scalability and practicality for real-world applications where robots are expected to operate in varied and visually distinctive settings.

Generative augmentation methods have made some strides in this direction by using generative models to augment datasets. However, these methods are complex, requiring extensive manual tuning, and often suffer from issues like performance bottlenecks and inaccuracies, particularly when dealing with segmentation from wrist camera views.

Green Screen Augmentation Methodology

The authors' novel approach involves data collection in environments that predominantly feature green screens. By applying chroma key technology—commonly used in the film industry to replace backgrounds—robotic training data can be augmented with diverse visual backgrounds. This stepwise process involves:

1. Green Screen Scene Setup: Establishing a green screen environment to cover non-relevant background objects during data collection.
2. Chroma Keying: Using chroma key algorithms to extract green screen masks and later augmenting these masks with various textures.
3. Training Robot Learning Policies: Applying this pre-processing to RGB-based robot learning methods to create robust policies capable of generalising across different scenes.

Evaluation and Results

The efficacy of was evaluated against several baselines, including no augmentation (NoAug), standard computer vision augmentation (CVAug), and generative augmentation methods. An extensive experimental setup with over 850 training demonstrations and 8200 evaluation episodes was used to test the proposed method's performance across eight challenging robotic manipulation tasks.

The results demonstrated that consistently outperformed the baseline methods:

• Improved success rate by 65% over NoAug
• Improved success rate by 29% over CVAug
• Improved success rate by 21% over generative augmentation

Furthermore, among the different variants of , the random texture variant () offered the best performance. Interestingly, the generative variant () did not perform as well despite using semantically meaningful backgrounds, suggesting that the variability of textures rather than semantic content might be the critical factor for successful generalisation.

Implications and Future Developments

The paper's findings advocate for a paradigm shift in data collection practices in robotic learning, emphasizing the utility of green screens and chroma key technology to generate diverse training data. This shift could markedly enhance the robots' ability to generalise across different environments, which is crucial for practical deployment.

Nonetheless, several limitations and future research directions are identified. The paper suggests exploring more advanced chroma key algorithms to improve mask quality, addressing generalisation to 6D poses, and extending to methods utilizing 3D observations. Additionally, combining with generative augmentation could help train world models capable of producing robust imaginary trajectories.

This research is poised to bridge significant gaps in scene generalisation within robot learning, highlighting substantial advances in how training data can be diversified to create more adaptable and capable robotic systems.