Enhancing Robotic Assembly with Symmetry-aware Reinforcement Learning

Core Concepts

The author proposes leveraging domain symmetry through data augmentation and auxiliary losses to enhance a recurrent SAC agent for solving symmetric POMDPs efficiently.

Abstract

This study focuses on improving robotic assembly tasks using a soft wrist by integrating data augmentation and auxiliary losses in training an actor-critic DRL agent. The proposed method leverages the potential symmetry of the task to achieve sample-efficient learning. Experimental evaluations across five symmetric peg shapes show promising results, with the proposed agent outperforming state-based agents in simulation. The approach allows for direct learning on real robots within a short timeframe, demonstrating the effectiveness of leveraging symmetry in robotic assembly tasks.

Stats

Results show that our proposed agent can be comparable to or even outperform a state-based agent.
Sample efficiency allows us to learn directly on the real robot within 3 hours.
Demonstrations are used to overcome reward sparsity.
The learned policies are better than human demonstrations.
The learned policy generalizes well across different peg shapes.

Quotes

"Our method's versatility extends beyond the peg-in-hole task with a soft wrist and can be adapted for other scenarios."
"The proposed agent outperforms state-based agents in simulation, showcasing the effectiveness of leveraging symmetry in robotic assembly tasks."

Key Insights Distilled From

Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist

by Hai Nguyen,T... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18002.pdf

Deeper Inquiries

How does imperfect symmetry affect the performance of the proposed method

Imperfect symmetry can have a significant impact on the performance of the proposed method. In scenarios where perfect symmetry is not achieved, such as surface unevenness or inconsistencies in real-world experiments, the agent may struggle to adapt effectively. Minor imperfections can lead to deviations from expected behaviors and outcomes, affecting the agent's ability to generalize across different shapes or environments. These deviations can introduce uncertainties that challenge the agent's decision-making process and hinder its overall performance. Therefore, it is crucial to address and account for these imperfections when implementing the proposed method in practical applications.

What are the implications of enforcing symmetry from the start on exploration capabilities

Enforcing symmetry from the start can have implications on exploration capabilities within a learning system. Strictly adhering to symmetrical constraints at all stages of training may limit the diversity of experiences encountered by the agent during exploration. This restriction could potentially hinder the agent's ability to discover novel strategies or solutions that deviate from perfectly symmetric patterns. As a result, enforcing symmetry too rigorously might restrict the exploration space, leading to suboptimal learning outcomes and reduced adaptability in complex and dynamic environments.

How can leveraging symmetries benefit learning in imperfect settings beyond robotic assembly tasks

Leveraging symmetries can offer several benefits for learning in imperfect settings beyond robotic assembly tasks. By incorporating symmetrical properties into learning algorithms, systems can exploit inherent regularities present in various domains, enhancing sample efficiency and generalization capabilities. Symmetry-aware approaches enable agents to leverage shared information across different states or actions efficiently, leading to more robust and adaptive learning processes. Additionally, utilizing symmetries allows for improved data augmentation techniques and regularization methods that enhance model performance even in noisy or uncertain environments.