On-Robot Learning With Equivariant Models (2203.04923v3)

Abstract: Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in the context of on-robot policy learning in which a policy must be learned entirely on a physical robotic system without reference to a model, a simulator, or an offline dataset. We focus on applications of Equivariant SAC to robotic manipulation and explore a number of variations of the algorithm. Ultimately, we demonstrate the ability to learn several non-trivial manipulation tasks completely through on-robot experiences in less than an hour or two of wall clock time.

• The paper establishes that employing discrete equivariant models, such as those based on D4 and C8, enhances sample efficiency in on-robot learning.
• The paper illustrates that incorporating data augmentation via buffer sampling further improves performance in challenging manipulation scenarios.
• The paper demonstrates that direct on-robot learning rapidly trains effective manipulation policies, questioning the necessity of sim2real transfer.

On-Robot Learning with Equivariant Models

The paper, "On-Robot Learning With Equivariant Models," addresses the challenges associated with training robotic policies directly on physical systems. Specifically, it explores the integration of equivariant neural networks into reinforcement learning paradigms, aiming to enhance sample efficiency and facilitate the learning of manipulation tasks directly on robots without relying on pre-trained models or simulators.

Overview and Contributions

Equivariant models have previously demonstrated superior sample efficiency in both computer vision and reinforcement learning tasks. This paper extends those insights to robotic manipulation, employing Equivariant Soft Actor-Critic (SAC) as a principal method. The crucial contributions of the paper are threefold:

1. Symmetry Group Selection: The research establishes that using equivariant models based on discrete symmetry groups, such as $D_4$ and $C_8$, is more effective than continuous groups ($SO(2)$ and $O(2)$) for robotic tasks. Although continuous symmetries might better encapsulate rotational and reflectional invariances in theory, discrete symmetries afford more practical benefits due to their regular representational capacity.
2. Data Augmentation: The authors show the additive benefit of data augmentation, even when using equivariant models. Incorporating augmented data samples, specifically through buffer augmentation, consistently improves the performance of models, notably in challenging tasks.
3. On-Robot Learning Evaluations: The paper demonstrates that employing equivariant models allows robots to effectively learn manipulation tasks from scratch in a real-world setting, achieving proficiency in less than two hours for various tasks. Importantly, it questions the necessity of sim2real transfer, suggesting that direct on-robot learning might surpass pre-trained policies in handling task-specific nuances of the physical environment.

Experimental Insights

The paper presents compelling experimental results across four manipulation tasks: Block Picking, Clutter Grasping, Block Pushing, and Block in Bowl. The experiments are conducted both in simulation and on a physical robot, offering a holistic view of the algorithm's effectiveness. Key findings include:

• Efficiency: In on-robot scenarios, the method achieves excellent performance within a range of 45 minutes to a couple of hours, depending on task complexity. For Block Picking and Clutter Grasping, the agent achieves near-perfect success rates rapidly.
• Comparison with Baselines: The paper benchmarks against the FERM framework, revealing superior learning curves for Equivariant SAC, especially in Block Pushing and Block in Bowl tasks, underlining the enhanced sample efficiency of equivariant models.
• Sim2real Transfer Analysis: The research challenges classical methodologies by illustrating scenarios where sim2real transfer is either redundant or detrimental, primarily due to qualitative discrepancies between simulated and real-world learning outcomes.

Implications and Future Directions

The practical implications of this work are significant, primarily in the domain of robotics where real-time learning typically incurs substantial costs. By reducing the need for extensive robotic hours and hardware wear, equivariant models potentially recalibrate current approaches to robotic learning sequences, minimizing dependencies on simulators or augmented experiences.

Theoretically, the paper invites further investigation into the optimal deployment of symmetry group structures in model architectures. Understanding when and why certain discrete symmetries outperform their continuous counterparts could refine the design of future neural architectures, fostering models that dynamically adjust their symmetry assumptions based on task-specific input.

Future work could explore autonomous determination of symmetry landscapes by the models themselves, reducing the designer's need to pre-specify these attributes. Additionally, integrating richer sensory inputs, such as RGB alongside depth data, could improve the robustness and applicability of learned policies to a broader set of robotic tasks, addressing transparency and reflectivity challenges.

In conclusion, this paper contributes a measured, insightful perspective on on-robot learning, advocating for the strategic application of symmetry-aware neural networks while providing strong empirical evidence to validate its proposition.