ASID: Active Exploration for System Identification in Robotic Manipulation (2404.12308v2)

Abstract: Model-free control strategies such as reinforcement learning have shown the ability to learn control strategies without requiring an accurate model or simulator of the world. While this is appealing due to the lack of modeling requirements, such methods can be sample inefficient, making them impractical in many real-world domains. On the other hand, model-based control techniques leveraging accurate simulators can circumvent these challenges and use a large amount of cheap simulation data to learn controllers that can effectively transfer to the real world. The challenge with such model-based techniques is the requirement for an extremely accurate simulation, requiring both the specification of appropriate simulation assets and physical parameters. This requires considerable human effort to design for every environment being considered. In this work, we propose a learning system that can leverage a small amount of real-world data to autonomously refine a simulation model and then plan an accurate control strategy that can be deployed in the real world. Our approach critically relies on utilizing an initial (possibly inaccurate) simulator to design effective exploration policies that, when deployed in the real world, collect high-quality data. We demonstrate the efficacy of this paradigm in identifying articulation, mass, and other physical parameters in several challenging robotic manipulation tasks, and illustrate that only a small amount of real-world data can allow for effective sim-to-real transfer. Project website at https://weirdlabuw.github.io/asid

• The paper presents a novel three-stage approach combining targeted exploration and system identification to enhance simulation fidelity in robotic manipulation.
• It leverages Proximal Policy Optimization for exploration and uses REPS and CEM to dynamically update simulation parameters, reducing sample complexity.
• Experimental results on tasks such as sphere manipulation and rod balancing demonstrate high precision in parameter estimation and effective policy transfer.

Enhancing Sim-to-Real Transfer in Robotic Manipulation Tasks Through Targeted Exploration

Introduction

In robotic systems, achieving efficient sim-to-real transfer is vital for practical deployments. The paper introduces a systematic methodology called Active Exploration for System Identification (ASID), which significantly enhances the fidelity of sim-to-real knowledge transfer. This approach amalgamates targeted exploration policies with system identification to update simulation parameters effectively, thus allowing robust policy training that can be directly deployed in real-world scenarios.

Methodology Overview

ASID operates under a three-stage framework:

Exploration Phase

The first stage is centered on data collection through targeted exploration. Exploration policies are designed to navigate the real environment optimally to collect trajectory data that maximizes the Fisher Information of unknown parameters. This ensures that trajectories are particularly sensitive to the parameters of interest, promoting efficient learning from a limited amount of data. The exploration policy is derived using Proximal Policy Optimization (PPO), guided by theoretical principles outlined in statistical estimation theory.

System Identification

Using the data obtained from the exploration phase, the system identification stage adapts the simulation model parameters to mirror the real environment more accurately. The approach leverages Relative Entropy Policy Search (REPS) and the Cross-Entropy Method (CEM) to fine-tune the simulation parameters based on the compiled real-world data.

Policy Optimization

The final stage involves training a robust control policy within the refined simulator. Once an accurate simulation model is established, standard reinforcement learning methods can be applied efficiently to train robust policies for complex manipulative tasks. These policies are then expected to transfer seamlessly to the real-world setup without further tuning.

Experimental Setup

Evaluation Metrics

The paper evaluates ASID across several robotic manipulation tasks:

• Sphere manipulation with unknown friction parameters.
• Rod balancing influenced by unidentified inertia distributions.
• Articulation recognition in jointed systems.

Each task highlights the necessity of exact parameter identification for successful task execution in real environments. For instance, in the sphere manipulation task, incorrect frictional estimates could lead to completely ineffective control strategies.

Results

The experimental outcomes emphasize ASID’s ability to learn effective exploration policies and achieve accurate system identification with minimal real-world interactions, substantially reducing the sample complexity traditionally associated with robust policy training in robotics. Specifically, in simulated environments, ASID outperformed baseline methods, including those employing random exploration or mutual information maximization without targeted exploration. It demonstrated precision in parameter estimation and consequently, high efficacy in task-specific policy learning.

Practical Implications

Implementing ASID in real-world robotic systems could substantially lower the barriers to deploying sophisticated robotic helpers in unstructured environments, such as homes or outdoor settings. By reducing the need for extensive data collection and manual tuning in real-world settings, ASID not only accelerates development cycles but also enhances the adaptability and reliability of robotic systems.

Future Directions

While the paper lays a robust foundation for effective sim-to-real transfer, future work could expand on several fronts. Extending ASID to accommodate multi-agent scenarios or more complex dynamic interactions could broaden its applicability. Moreover, integrating more advanced model-based reinforcement learning techniques might yield further enhancements in simulation fidelity and task execution performance.

Conclusion

ASID represents a significant step forward in leveraging simulation environments for robust real-world robotic control. By systematically addressing the exploration and system identification phases with theoretically grounded strategies, ASID allows for efficient and reliable policy learning, pivotal for the next generation of robotic systems in diverse applications.