Symmetry Considerations for Learning Task Symmetric Robot Policies (2403.04359v1)

Abstract: Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively. Often, the learned behaviors fail to achieve the desired transformation invariances and suffer from motion artifacts. For instance, a quadruped may exhibit different gaits when commanded to move forward or backward, even though it is symmetrical about its torso. This issue becomes further pronounced in high-dimensional or complex environments, where DRL methods are prone to local optima and fail to explore regions of the state space equally. Past methods on encouraging symmetry for robotic tasks have studied this topic mainly in a single-task setting, where symmetry usually refers to symmetry in the motion, such as the gait patterns. In this paper, we revisit this topic for goal-conditioned tasks in robotics, where symmetry lies mainly in task execution and not necessarily in the learned motions themselves. In particular, we investigate two approaches to incorporate symmetry invariance into DRL -- data augmentation and mirror loss function. We provide a theoretical foundation for using augmented samples in an on-policy setting. Based on this, we show that the corresponding approach achieves faster convergence and improves the learned behaviors in various challenging robotic tasks, from climbing boxes with a quadruped to dexterous manipulation.

• The paper introduces symmetry integration in DRL through data augmentation and mirror loss to achieve robust task-symmetric robot policies.
• It employs stabilized trajectory mirroring and careful network initialization to improve convergence, yielding higher episodic returns with lower variance.
• Empirical results across multiple tasks and hardware tests demonstrate practical gains in obstacle navigation and dexterous manipulation.

Symmetry Considerations for Learning Task Symmetric Robot Policies

The paper explores incorporating symmetry invariance into deep reinforcement learning (DRL) for enhancing robot policy learning, particularly in scenarios involving symmetric task requirements. Traditional DRL has not been efficient in exploiting symmetry, leading to suboptimal policies that may not exhibit desired transformation invariances, especially evident in high-dimensional and complex environments. This paper proposes two primary methods for integrating symmetry into the training process: data augmentation and mirror loss functions.

Methodological Insights

The primary focus of the research is on task-level symmetry rather than motion-level symmetry. Conventional investigations of symmetry in robotic learning predominantly focus on symmetry in motion, such as gaits and periodic movements. However, this paper innovatively addresses goal-conditioned tasks, where the symmetry lies in the task execution. The authors argue that achieving this form of symmetry does not inherently demand motion symmetry, allowing for asymmetric motion execution suitable for complex tasks such as obstacle navigation and dexterous manipulation.

1. Mirror Loss Function: This approach augments the learning objective by adding a penalty that targets asymmetricity in the policy. While theoretically straightforward, it introduces challenges in balancing between task optimization and symmetry enforcement.
2. Symmetry-Based Data Augmentation: Building on the success of data augmentation in classical deep learning, this method involves augmenting collected trajectories with their symmetrical counterparts. The authors re-formulate the augmentation to stabilize learning by retaining the action probability of original samples instead of mirroring them, thus mitigating issues stemming from potential instabilities caused by non-symmetric but high-performing trajectories.

Empirical Evaluation

The research evaluates these methods across four diverse robotic tasks—ranging from balancing a cart-pole to complex dexterous manipulation—demonstrating the efficacy of symmetry considerations:

• CartPole and ANYmal-Climb Tasks: Policies trained with symmetry augmentation displayed superior convergence and stability, reinforcing the theoretical claims. Training performance highlighted that symmetry augmentation leads to higher average episodic returns and lower variance in returns for symmetric task versions.
• ANYmal-Push and Trifinger-Repose Tasks: In scenarios demanding complex manipulations, augmentation yielded policies that utilized robotic limbs more effectively, showcasing robustness across task variants.
• Assessment of Network Initialization: Symmetric policy learning was sensitive to network initialization, indicating that initialization impacts the policy's capacity to leverage symmetry properly through augmentation. Networks initialized with small weights conformed well to symmetries, substantiating the necessity for careful initialization strategies.

Key Findings and Implications

The research crucially reveals that symmetry-based data augmentation provides the most consistent performance across tasks, outpacing mirror loss implementations in achieving task symmetry. These findings underscore the practicality of symmetry augmentation for crafting robust and efficient policies, highlighting its potential for application in tasks beyond the ones examined here.

From a practical perspective, the paper demonstrates how symmetry-guided learning can translate effectively to real-world robotic deployments, as seen in the hardware tests with the ANYmal robot. The methodology proved resilient in dealing with the inherent asymmetries found in real hardware systems, suggesting its utility beyond the controlled environment of simulations.

Future Directions

The implications of this research on symmetry in robot learning systems suggest several avenues for further exploration. There is a fertile ground for developing techniques that self-discover symmetry in complex environments where explicit transformations are not readily defined. Moreover, the interplay between symmetry in latent spaces, obtainable through autoencoders or other generative models, presents an intriguing prospect. Addressing these challenges will significantly deepen AI's capability in context-aware, symmetry-exploiting learning.

In conclusion, this work articulates a compelling case for the integration of symmetry in advanced robot control systems, bridging a crucial gap in existing DRL methodologies. The research paves the way for deploying symmetry-aware learning systems that are efficient, robust, and seamlessly adaptive to diverse real-world scenarios.