Grounded Curriculum Learning (2409.19816v1)

Abstract: The high cost of real-world data for robotics Reinforcement Learning (RL) leads to the wide usage of simulators. Despite extensive work on building better dynamics models for simulators to match with the real world, there is another, often-overlooked mismatch between simulations and the real world, namely the distribution of available training tasks. Such a mismatch is further exacerbated by existing curriculum learning techniques, which automatically vary the simulation task distribution without considering its relevance to the real world. Considering these challenges, we posit that curriculum learning for robotics RL needs to be grounded in real-world task distributions. To this end, we propose Grounded Curriculum Learning (GCL), which aligns the simulated task distribution in the curriculum with the real world, as well as explicitly considers what tasks have been given to the robot and how the robot has performed in the past. We validate GCL using the BARN dataset on complex navigation tasks, achieving a 6.8% and 6.5% higher success rate compared to a state-of-the-art CL method and a curriculum designed by human experts, respectively. These results show that GCL can enhance learning efficiency and navigation performance by grounding the simulation task distribution in the real world within an adaptive curriculum.

• The paper introduces an adaptive curriculum that grounds simulated tasks in real-world dynamics to improve reinforcement learning performance.
• It employs a dual-agent framework with a teacher guiding a student under a POMDP and utilizes a VAE for realistic task representation.
• Experimental results on the BARN dataset demonstrate 6.8% higher success rates compared to state-of-the-art and expert-designed curricula.

Insightful Overview of Grounded Curriculum Learning

The paper presents an approach called Grounded Curriculum Learning (GCL) aimed at addressing critical mismatches between simulated environments and real-world conditions in robotics, particularly in reinforcement learning (RL) contexts. The approach is designed to enhance learning efficiency and generalization performance for robotic systems transitioning from simulation to real-world task execution.

Context and Problem Statement

In the field of robotic reinforcement learning, simulators serve as essential tools by providing a controlled and cost-efficient platform for gathering training data. However, despite previous advances in achieving a closer simulation-to-reality (sim-to-real) alignment in terms of dynamics, there's an overlooked discrepancy in the distribution of training tasks across simulations and real-world applications. Traditional curriculum learning (CL) strategies tend to adjust the difficulty of training tasks but often fail to ground these tasks effectively in real-world prospects, which can compromise an RL agent's post-deployment performance.

Contribution and Methodology

GCL is proposed as a refined approach for rooting curriculum learning in real-world task distributions by introducing an adaptive framework that features a simulated task sequence reflective of real-world conditions. The approach is characterized by several key components that collectively improve task relevance, learning efficiency, and navigation performance for RL agents:

1. Simulation Realism: GCL aligns simulated tasks with real-world scenarios to ensure learning relevance.
2. Task Awareness: It employs a Variational Autoencoder (VAE) for task representation and generates diverse task distributions that conform to real-world criteria. This element ensures that the simulated tasks bear resemblance to actual environments encountered by the robots.
3. Student Performance Monitoring: Real-time evaluations of robot performance help in dynamically adjusting the curriculum, ensuring that training remains efficient and effective.

The framework employs a hierarchical dual-agent setup: a fully informed teacher agent, which guides the learning process by selecting and adapting the task complexity, and a student agent operating under a Partially Observable Markov Decision Process (POMDP). The teacher agent generates tasks both from real-world examples and through its learned model to foster adaptive curriculum learning.

Experimental Evaluation

GCL was validated using the Benchmark Autonomous Robot Navigation (BARN) dataset, a standard in evaluating robotic navigation efficacy under constrained conditions. The paper reports that GCL attained a 6.8% and 6.5% higher success rate relative to a state-of-the-art curriculum learning method and a curriculum manually designed by human experts, respectively. This improved performance highlights GCL's ability to enhance learning efficiency and real-world navigation adaptability by aligning simulated learning experiences with realistic conditions.

Implications and Future Directions

The implication of GCL is profound in bridging the gap between controlled simulation settings and variable real-world deployments. The approach's adaptive curriculum paves the way for RL applications that better generalize from the simulation phase to real-world tasks. This innovation is particularly promising for sectors such as autonomous driving, delivery robotics, and other domains relying on precise navigation and task execution in complex environments.

As future research directions, exploring GCL's applicability across a broader set of robotic tasks beyond navigation could yield further insights. Additionally, the framework could incorporate advanced transfer learning techniques to adapt pre-trained models to new tasks or environments, thereby enhancing the scope and utility of autonomous robotic systems in increasingly diverse real-world conditions.