JUICER: A Data-Efficient Imitation Learning Pipeline for Precise Long-Horizon Robotic Assembly

Grunnleggende konsepter

A pipeline combining expressive policy architectures, synthetic data augmentation, and iterative model improvement enables learning high-performance policies for precise long-horizon robotic assembly from a small number of human demonstrations.

Sammendrag

The paper proposes JUICER, a data-efficient imitation learning pipeline for learning precise long-horizon robotic assembly tasks from a small number of human demonstrations.
Key highlights:

The pipeline combines an expressive diffusion policy architecture, techniques for synthetic dataset expansion around "bottleneck" states requiring high precision, and an iterative "collect-and-infer" approach to incrementally improve the policy.
Experiments on four furniture assembly tasks in simulation demonstrate the effectiveness of the pipeline, with the combined approach outperforming vanilla imitation learning baselines.
The authors show that JUICER can achieve high performance (e.g., 71% success rate) on the one leg assembly task using just 10 human demonstrations, compared to 19% with the baseline.
The paper also investigates the impact of multitask training, showing that a single model trained on a mix of tasks can outperform individual task-specific models.
The key challenges addressed include fitting complex demonstrated behaviors from raw images, learning robust behaviors around critical "bottleneck" regions, and efficiently expanding the dataset without additional human effort.

Statistikk

The paper does not provide any specific numerical data or metrics to support the key claims. The results are presented in the form of success rates for different tasks and methods.

Sitater

"Our pipeline combines expressive policy architectures and various techniques for dataset expansion and simulation-based data augmentation. These help expand dataset support and supervise the model with locally corrective actions near bottleneck regions requiring high precision."
"We demonstrate our pipeline on four furniture assembly tasks in simulation, enabling a manipulator to assemble up to five parts over nearly 2500 time steps directly from RGB images, outperforming imitation and data augmentation baselines."

Viktige innsikter hentet fra

JUICER

by Lars Ankile,... klokken arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03729.pdf

Dypere Spørsmål

How can the proposed trajectory augmentation technique be extended to handle dynamic objects and non-insertion-based bottleneck states

The trajectory augmentation technique proposed in the study can be extended to handle dynamic objects and non-insertion-based bottleneck states by incorporating more sophisticated data augmentation strategies. For dynamic objects, the augmentation process can involve simulating various dynamic scenarios where the object's position, orientation, or behavior changes unpredictably. By introducing variability in the object's dynamics during training, the model can learn to adapt to different object movements and interactions. This can be achieved by incorporating physics-based simulations or generative models that can simulate dynamic object behaviors.
To address non-insertion-based bottleneck states, the trajectory augmentation process can be tailored to focus on specific challenging states or transitions in the assembly process. By identifying critical states that require high precision or specific actions, the augmentation can generate synthetic data around these states to provide additional training examples for the model. This can involve perturbing the environment, introducing obstacles or constraints, or simulating variations in the task setup to cover a broader range of challenging scenarios beyond simple insertions.

What are the potential challenges and limitations of directly transferring the learned policies from simulation to the real world, and how could the pipeline be adapted to address those

The direct transfer of learned policies from simulation to the real world poses several challenges and limitations that need to be addressed for successful deployment. One major challenge is the reality gap, where the simulation environment may not accurately capture all the complexities and uncertainties present in the real world. This can lead to discrepancies in the model's performance when deployed in a real-world setting. To address this, the pipeline can be adapted by incorporating domain adaptation techniques that help bridge the gap between simulation and reality. This can involve fine-tuning the model on real-world data, using domain randomization to simulate diverse real-world conditions, or leveraging transfer learning approaches to adapt the model to the target environment.
Another limitation is the lack of generalizability of the learned policies to unseen scenarios or tasks in the real world. To overcome this, the pipeline can be extended to include continual learning or meta-learning strategies that enable the model to adapt and generalize to new tasks or environments. By incorporating mechanisms for adaptive learning and knowledge transfer, the model can continuously improve and adapt to novel challenges in the real world.

Given the potential benefits of multitask learning observed in this work, how could the pipeline be further extended to enable seamless transfer of skills across a broader range of assembly tasks

To further leverage the benefits of multitask learning and enable seamless transfer of skills across a broader range of assembly tasks, the pipeline can be extended in several ways. One approach is to incorporate a hierarchical or modular architecture that allows the model to learn task-specific skills while sharing common knowledge across tasks. By structuring the learning process hierarchically, the model can capture task-specific nuances while benefiting from shared representations and knowledge.
Additionally, the pipeline can be enhanced with curriculum learning techniques that gradually expose the model to increasingly complex or diverse tasks. By designing a curriculum of tasks with varying levels of difficulty, the model can learn progressively and build upon previously acquired skills. This gradual learning process can improve the model's ability to transfer knowledge and skills across a wide range of assembly tasks.
Furthermore, the pipeline can benefit from meta-learning approaches that enable the model to quickly adapt to new tasks with limited data. By training the model to learn how to learn efficiently, it can acquire new tasks with minimal human intervention and rapidly generalize to unseen scenarios. Meta-learning can enhance the model's flexibility and adaptability, making it more robust and capable of handling a diverse set of assembly tasks.