Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Summary: arXiv:2604.03523v1 Announce Type: cross
Abstract: Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high collection cost. Furthermore, imitation learning algorithms assume that the data is independently and identically distributed, which ultimately results in poorer performance as gradual errors emerge and compound within test-time trajectories. We address these issues by introducing the “master your own expertise” (MYOE) framework, a self-imitation framework that enables robotic agents to learn complex behaviors from limited demonstration data samples.
Introduction
The advancement of robotic systems relies heavily on effective learning algorithms that can mimic human-like behavior. Traditional methods of reinforcement learning from demonstrations (RLfD) generally depend on a large amount of expert data, which is often not feasible in practical applications. The scarcity of high-quality data can lead to significant challenges in training agents to perform complex tasks.
The MYOE Framework
To overcome the limitations associated with data scarcity, we propose the “master your own expertise” (MYOE) framework. This innovative approach allows robotic agents to learn from limited demonstration data by leveraging self-imitation. MYOE is designed to enhance the learning capability of robots in environments where data collection is expensive or time-consuming.
Queryable Mixture-of-Preferences State Space Model (QMoP-SSM)
Central to our approach is the development of the queryable mixture-of-preferences state space model (QMoP-SSM). This model is instrumental in estimating the desired goals of the robotic agent at each time step. By continuously evaluating these goals, we can better align the agent’s actions with intended outcomes.
Preference Regret Optimization
One of the key components of our framework is the computation of “preference regret.” This metric measures the discrepancy between the agent’s performance and the optimal behavior defined by the desired goals. By minimizing preference regret, we can significantly improve the robot’s control policy and enhance its overall performance.
Experimental Results
To validate our approach, we conducted a series of experiments comparing our MYOE framework with other state-of-the-art RLfD schemes. The results indicated that our agent demonstrated:
- Robustness: The MYOE framework exhibited resilience against varying conditions and noise in the data.
- Adaptability: The agent was able to adjust its behavior based on limited input, showcasing flexibility in learning.
- Out-of-Sample Performance: Our method outperformed competitors even in scenarios not covered during training.
Conclusion
The introduction of the MYOE framework and the QMoP-SSM model represents a significant advancement in the field of robotic learning. By addressing the challenges posed by limited demonstration data, we pave the way for more effective and efficient robotic systems. For those interested in exploring this work further, the supporting GitHub repository can be found at: GitHub Repository.