Simulation-Aided Policy Tuning for Black-Box Robot Learning (2411.14246v1)

Abstract: How can robots learn and adapt to new tasks and situations with little data? Systematic exploration and simulation are crucial tools for efficient robot learning. We present a novel black-box policy search algorithm focused on data-efficient policy improvements. The algorithm learns directly on the robot and treats simulation as an additional information source to speed up the learning process. At the core of the algorithm, a probabilistic model learns the dependence of the policy parameters and the robot learning objective not only by performing experiments on the robot, but also by leveraging data from a simulator. This substantially reduces interaction time with the robot. Using this model, we can guarantee improvements with high probability for each policy update, thereby facilitating fast, goal-oriented learning. We evaluate our algorithm on simulated fine-tuning tasks and demonstrate the data-efficiency of the proposed dual-information source optimization algorithm. In a real robot learning experiment, we show fast and successful task learning on a robot manipulator with the aid of an imperfect simulator.

• The paper develops a simulation-aided black-box policy search algorithm that integrates Bayesian optimization to boost data efficiency in robotic learning.
• It demonstrates that combining simulation data with real-world experiments significantly accelerates policy tuning for robotic manipulators.
• The approach paves the way for rapid industrial deployment by minimizing costly physical interactions during robot learning.

Simulation-Aided Policy Tuning for Black-Box Robot Learning

The paper "Simulation-Aided Policy Tuning for Black-Box Robot Learning" introduces a novel approach to enhancing robotic learning through the integration of a simulation-aided black-box policy search algorithm. This research focuses explicitly on the critical problem of data efficiency in robotic learning, a domain where excessive interactions with the real environment can be costly and time-intensive. By leveraging simulations as a supplementary information source, the authors mitigate the data requirements substantially, which is a significant advancement in the field of robot learning.

Central to the methodology proposed is the integration of Bayesian optimization within the framework of the algorithm. This probabilistic modeling approach capitalizes on both empirical data from physical experiments and simulated data, allowing for a robust optimization of policy parameters. The duality of information sources enables the algorithm to perform policy updates that guarantee improvements with high probability, thus optimizing learning efficiency and speed.

The paper presents a thorough evaluation of the proposed algorithm through simulated fine-tuning tasks and an empirical real-world robotic experiment. The results convincingly demonstrate the effectiveness of the dual-information source optimization. Specifically, in the robotic experiments using a manipulator, the integration of imperfect simulation data significantly expedites the learning process without compromising the learning curve or final performance. This is a notable accomplishment, especially in contexts where accurate simulation environments are either challenging to construct or unavailable.

Several implications arise from this paper. Practically, the use of the simulation-aided approach can lead to more rapid deployment of robot learning in industrial settings, where machine downtime and learning time equate to operational costs. Theoretically, the paper sets the stage for further exploration into the combination of data from multiple information sources in robotic learning frameworks. Future work might explore extending this approach to more complex multi-agent interactions or continuous learning scenarios where environmental dynamics are highly variable.

Such advancements pave the way for more sophisticated applications in real-time adaptive systems, expanding the potential of robotics in new domains. As AI technology develops, enhancements in these algorithms will likely lead to even more efficient and adaptive systems, furthering the scope and capabilities of autonomous learning in robotics.