Learning Latent Plans from Play (1903.01973v2)

Abstract: Acquiring a diverse repertoire of general-purpose skills remains an open challenge for robotics. In this work, we propose self-supervising control on top of human teleoperated play data as a way to scale up skill learning. Play has two properties that make it attractive compared to conventional task demonstrations. Play is cheap, as it can be collected in large quantities quickly without task segmenting, labeling, or resetting to an initial state. Play is naturally rich, covering ~4x more interaction space than task demonstrations for the same amount of collection time. To learn control from play, we introduce Play-LMP, a self-supervised method that learns to organize play behaviors in a latent space, then reuse them at test time to achieve specific goals. Combining self-supervised control with a diverse play dataset shifts the focus of skill learning from a narrow and discrete set of tasks to the full continuum of behaviors available in an environment. We find that this combination generalizes well empirically---after self-supervising on unlabeled play, our method substantially outperforms individual expert-trained policies on 18 difficult user-specified visual manipulation tasks in a simulated robotic tabletop environment. We additionally find that play-supervised models, unlike their expert-trained counterparts, are more robust to perturbations and exhibit retrying-till-success behaviors. Finally, we find that our agent organizes its latent plan space around functional tasks, despite never being trained with task labels. Videos, code and data are available at learning-from-play.github.io

An Analysis of "Learning Latent Plans from Play"

The paper "Learning Latent Plans from Play" addresses the significant challenge in robotics of acquiring a diverse set of general-purpose skills. The authors introduce a novel approach, self-supervised learning from human teleoperated play data, to effectively scale skill acquisition for robotic systems. Their method, Play-LMP, stands in contrast to conventional task demonstration techniques, offering several distinct advantages.

The central premise of this work lies in utilizing play as a data collection method, leveraging its cost-efficiency and richness. Play data can be collected without the need for manual task segmenting, labeling, or resetting, providing an extensive and varied interaction space. The authors demonstrate that play data covers approximately four times more of the interaction space than traditional task demonstrations for a similar amount of collection time.

Play-LMP operates by organizing the behaviors observed during play into a latent space, which is then used to achieve specific goals. This approach shifts the focus from learning a narrow set of tasks to a continuum of behaviors available in an environment, thereby generalizing skill learning. The paper reports that after self-supervision on unlabeled play data, Play-LMP significantly outperforms individually expert-trained policies on 18 visual manipulation tasks in a simulated robotic environment. Notably, play-supervised models display robustness to perturbations and a retrying behavior until success is achieved.

Key Contributions and Findings

• Play Data Utilization: The authors advocate for the use of play data, emphasizing its richness and coverage. They highlight how play allows robots to explore a wider range of object interactions, which is crucial for developing general-purpose policy models.
• Latent Space Representation: Play-LMP's architecture consists of stochastic encoders that shape a latent plan space through plan recognition and proposal. The model minimizes the KL divergence between the predictions of these encoders, ensuring that the inferred plans align with actual behaviors observed during play.
• Empirical Evaluation: The paper's empirical results indicate Play-LMP's superiority over expert-trained policies on a broad spectrum of tasks. The success rates reported underscore the potential of this self-supervised approach in achieving robustness and adaptability in robotic manipulation.
• Robustness and Emergent Behaviors: Models trained on play data were found to be more resilient to changes in initial conditions than those trained on curated demonstrations. Additionally, the retrying behavior emerged naturally, attributed to the diverse and continuous nature of play data.

Implications and Future Directions

This work has significant theoretical and practical implications. By demonstrating the efficacy of self-supervised learning from play data, it challenges the prevailing norms in robotic skill acquisition. The findings suggest that leveraging naturally occurring, unsegmented interactions can lead to more versatile and resilient robotic systems.

For future developments, the authors indicate potential exploration into novel environments and objects, assessing the generalization capabilities of Play-LMP. Addressing variability in play data distributions is also a concern, particularly in scenarios where certain interactions may dominate over others, potentially leading to representational bias.

Moreover, the interplay between representation learning and control underscores a promising avenue for further research. Refining the latent space and exploring different encoder models could enhance the method's ability to capture complex multimodal behaviors.

Conclusion

"Learning Latent Plans from Play" presents a compelling argument for rethinking how robotic skills are acquired. By harnessing human intuition through play, the paper sets a new direction in self-supervised learning for robotics. The robustness, generality, and efficiency demonstrated by Play-LMP offer a promising step towards developing multifunctional robotic agents capable of adapting to a wide array of tasks and environments.