Gaussian Processes for Data-Efficient Learning in Robotics and Control (1502.02860v2)

Abstract: Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

• The paper develops PILCO, a nonparametric reinforcement learning framework that uses Gaussian processes to achieve data-efficient policy search in robotics and control.
• It employs full probabilistic prediction to account for model uncertainty and enhance long-term planning in dynamic environments.
• Experimental results demonstrate that PILCO's rapid learning capability outperforms traditional parametric models in challenging, data-scarce scenarios.

Fast Interactive Learning in Nonparametric Models for Robotics and Control

The paper "Fast Interactive Learning in Nonparametric Models for Robotics and Control" presents a novel reinforcement learning (RL) framework known as PILCO. It aims to address the challenge of data efficiency in RL by leveraging a probabilistic Gaussian process (GP) dynamics model. This GP-based approach provides a flexible and capable model for data-efficient policy search, particularly within the domains of robotics and control.

Key Contributions

The primary contribution of this work is the development and validation of PILCO, a RL framework that excels in scenarios where data collection is both time-consuming and expensive. Traditional parametric models often struggle with model errors that significantly affect the learning process, particularly over longer horizons. PILCO mitigates these issues by utilizing a non-parametric GP model that supports full probabilistic predictions.

1. Non-parametric Dynamics Model: Unlike traditional models, PILCO does not assume a fixed structure for system dynamics. Instead, it relies on a GP model to learn these dynamics, offering high flexibility and adaptability to varied tasks.
2. Incorporation of Model Uncertainty: The use of GPs allows PILCO to account for uncertainty in model predictions, providing robust long-term planning capabilities. This approach enables PILCO to perform effectively even in uncertain and dynamic environments.
3. Data Efficiency: One of the strongest results highlighted is PILCO's ability to learn rapidly without requiring extensive interaction data. This efficiency is particularly advantageous for robotics and control applications, where data collection can be prohibitively slow or expensive.

Experimental Validation

The paper substantiates the efficacy of PILCO through a series of experiments on both simulated and real-world control tasks. These experiments demonstrate that PILCO can outperform existing methods, especially in contexts where prior knowledge of the system dynamics is limited or absent.

1. Rapid Learning: The results indicate that PILCO can achieve significant performance improvements over competing strategies, with fewer interactions.
2. Real-world Applications: In practical control and robotics scenarios, PILCO's learning speed and reliability make it a compelling choice for deploying RL in real-time and resource-constrained environments.

Implications and Future Work

The introduction of PILCO has significant implications for the field of RL in robotics and control. By reducing the data requirements and improving learning efficiency, it opens up possibilities for applying RL to more complex and less predictable environments.

Future developments may focus on enhancing scalability and extending the framework to handle higher-dimensional tasks more efficiently. Additionally, integrating PILCO with other advanced methodologies, such as deep learning, could further enhance its capabilities and application scope in AI. The fusion of these approaches could lead to significant advancements in autonomous systems and intelligent control mechanisms.

In conclusion, PILCO emerges as a promising approach in the pursuit of data-efficient RL, providing a robust framework that addresses key limitations of traditional methods and paving the way for broader adoption in complex, real-world applications.