Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning (1912.11032v1)

Abstract: Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements. Many practical tasks require manipulation of multiple objects, and the complexity of such tasks increases with the number of objects. Learning from a curriculum of increasingly complex tasks appears to be a natural solution, but unfortunately, does not work for many scenarios. We hypothesize that the inability of the state-of-the-art algorithms to effectively utilize a task curriculum stems from the absence of inductive biases for transferring knowledge from simpler to complex tasks. We show that graph-based relational architectures overcome this limitation and enable learning of complex tasks when provided with a simple curriculum of tasks with increasing numbers of objects. We demonstrate the utility of our framework on a simulated block stacking task. Starting from scratch, our agent learns to stack six blocks into a tower. Despite using step-wise sparse rewards, our method is orders of magnitude more data-efficient and outperforms the existing state-of-the-art method that utilizes human demonstrations. Furthermore, the learned policy exhibits zero-shot generalization, successfully stacking blocks into taller towers and previously unseen configurations such as pyramids, without any further training.

• The paper demonstrates that incorporating graph neural networks into relational reinforcement learning significantly improves data efficiency in multi-object manipulation.
• It introduces a curriculum learning strategy with a sequential approach that progressively increases task difficulty, enhancing performance and generalization.
• Experimental validation shows a 75% success rate in stacking six blocks without demonstrations, outperforming state-of-the-art methods that use billions of steps.

Relational Reinforcement Learning for Multi-Object Manipulation

The paper entitled "Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning" presents a novel approach to the complex problem of learning multi-object manipulation using reinforcement learning (RL) methods. Traditional RL approaches for robotic manipulation face significant challenges, especially in scenarios requiring the manipulation of multiple objects. This paper introduces a relational reinforcement learning framework that utilizes a graph-based neural network architecture to overcome these challenges and improve both data efficiency and task generalization.

Core Contributions

The authors propose a method based on graph neural networks (GNNs), which they term ReNN (Relational Neural Network), optimized using Soft Actor-Critic (SAC) with Hindsight Experience Replay (HER). Fundamental to their approach is the use of relational inductive biases, which enable the model to learn effectively from a curriculum of progressively more challenging tasks. The relational architecture is particularly important for handling tasks with varying numbers and configurations of objects, supporting zero-shot generalization capabilities.

Experimental Validation

• Environment Setup: The authors designed a simulation environment using a 7-DoF Fetch robot arm to evaluate the proposed method on block stacking tasks. The robot manipulates up to nine blocks with step-wise sparse rewards, which are more challenging but robust against exploitation by trivial solutions.
• Curriculum Learning: Three curricula were tested—Direct, Uniform, and Sequential—with the latter proving to be crucial for success when stacking larger numbers of blocks. The Sequential curriculum introduces complexity gradually by increasing the number of blocks only when prior tasks are mastered.
• Performance Metrics: Compared to previous methods that require human demonstrations, the ReNN approach demonstrated significant improvements in data efficiency. For instance, without any demonstrations, their system reached a 75% success rate at stacking six blocks with only 30 million environment steps, compared to 32% for the state-of-the-art with over 2.3 billion steps using demonstrations.

Zero-Shot Generalization

A standout capability of the ReNN framework is its ability to generalize to new configurations without further training. The researchers evaluated generalization on unseen tasks including constructing pyramids and multiple towers. The ReNN's architecture and its attention mechanism allow the policy to leverage learned relational features, enabling it to tackle different task configurations successfully.

Implications and Future Directions

From a theoretical standpoint, the incorporation of relational inductive biases via GNNs marks a significant advancement in the RL field. By simulating real-world scenarios where robots must interact with complex environments, this research also contributes practical implications for automated systems across various domains, such as logistics and manufacturing.

In future work, extending these methods to include visual inputs could bridge the gap between simulation and real-world applications, enhancing the generalization capability even further. Moreover, developing automated discovery methods for task curricula could eliminate manual intervention, making these techniques more broadly applicable.

Conclusion

The paper effectively demonstrates that relational learning architectures combined with curriculum learning can significantly enhance the ability of RL agents to perform multi-object manipulation tasks. By leveraging the structural representation power of GNNs, this work provides a foundation for more sophisticated autonomous systems capable of complex decision-making and generalization in dynamic environments.