Policy learning in SE(3) action spaces (2010.02798v2)

Abstract: In the spatial action representation, the action space spans the space of target poses for robot motion commands, i.e. SE(2) or SE(3). This approach has been used to solve challenging robotic manipulation problems and shows promise. However, the method is often limited to a three dimensional action space and short horizon tasks. This paper proposes ASRSE3, a new method for handling higher dimensional spatial action spaces that transforms an original MDP with high dimensional action space into a new MDP with reduced action space and augmented state space. We also propose SDQfD, a variation of DQfD designed for large action spaces. ASRSE3 and SDQfD are evaluated in the context of a set of challenging block construction tasks. We show that both methods outperform standard baselines and can be used in practice on real robotics systems.

• The paper introduces ASRSE3 to transform high-dimensional SE(3) actions into sequential sub-actions for scalable robotic manipulation.
• It integrates ASRSE3 with deep Q-learning, partitioning action selection hierarchically to significantly enhance computational efficiency.
• It refines imitation learning with SDQfD, steering exploration towards expert actions to improve performance in robotic block-stacking tasks.

Policy Learning in $SE(3)$ Action Spaces

The paper presents a novel approach to reinforcement learning in high-dimensional action spaces, primarily focusing on robotic manipulation tasks within $SE(3)$ spaces. Typical reinforcement learning applications in robotics leverage low-dimensional action spaces, hampering the ability to tackle complex manipulation tasks effectively. To address these limitations, the authors propose two key methodologies: ASRSE3 (Augmented State Representation for $SE(3)$) and SDQfD (Strict Deep Q-learning from Demonstrations).

Contributions and Methodologies

The authors introduce the Augmented State Representation (ASRSE3) as a means to convert a high-dimensional action space into an equivalent, reduced-action-space problem while augmenting the state space. This transformation allows for effectively addressing the higher dimensional poses in $SE(3)$, encompassing six degrees of freedom—specifically for robotic arms—by partitioning actions into sequential selections of sub-actions. This hierarchical treatment of action selection helps maintain computational feasibility while capturing the complexity required for nuanced manipulation tasks.

ASRSE3 integrates seamlessly with well-established reinforcement learning algorithms, exemplified by its coupling with Deep Q-Learning. Hierarchically structured $Q$ functions are optimized for sub-actions, allowing for efficient computation across stages of the action space.

The second contribution, SDQfD, modifies the existing DQfD framework to handle the vast action space more efficiently. By implementing a stricter margin loss that penalizes all sub-optimal actions beyond a certain threshold, SDQfD aims to steer value updates towards expert-like actions more aggressively, thereby facilitating learning in environments with sparse rewards.

Experimental Results

The authors evaluate ASRSE3 and SDQfD through a series of experiments involving both simulated and real-world robotic block-stacking tasks. By operating in diverse block-stacking environments, with variations in object geometry and positioning complexities, the methodologies are rigorously tested on their ability to generalize and perform under dynamic conditions. Numerical results show substantial improvements in task execution speed and accuracy compared to baseline models such as standard DQfD, ADET, and model-free DQN approaches.

Notably, ASRSE3 DQN exhibits superior capabilities in scenarios requiring precise action orientations and manipulations. SDQfD further demonstrates enhanced performance due to its guided exploration strategy derived from imitation learning. Together, ASRSE3 SDQfD achieves high success rates in constructing complex block structures, even when extended to previously unseen object shapes and sizes.

Implications and Future Directions

The development of ASRSE3 and SDQfD has significant implications for practical industrial and robotic applications. Enhanced handling of $SE(3)$ action spaces optimizes robotic manipulations, making feasible the deployment of robots in varied environments that require flexibility and rapid adaptability, such as assembly lines and household settings.

The inclusion of an augmented state representation provides a pathway for extending this approach to other domains that involve high-dimensional control tasks alongside robotic manipulation, such as autonomous navigation within complex terrains. Furthermore, the integration of broader ranges of object manipulation and orientation within these frameworks points toward a future where robotic systems can adapt more seamlessly to real-world unpredictabilities.

In terms of theoretical contributions, this paper's findings emphasize the advantages of hierarchical action space treatments in reinforcement learning, particularly when combined with structured loss mechanisms, opening up avenues for additional research into more scalable and robust reinforcement learning models for high-dimensional tasks.

Conclusion

This paper contributes significantly to the field of robotic manipulation and reinforcement learning within high-dimensional spaces, exemplifying how the combination of action space restructuring and advanced imitation learning techniques can lead to superior performance in complex robotic tasks. Future work may address existing scalability limitations and explore extending these methodologies across broader application spaces while retaining the crucial balance between computational feasibility and task complexity.