Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions (2209.07899v3)

Abstract: Learning diverse skills is one of the main challenges in robotics. To this end, imitation learning approaches have achieved impressive results. These methods require explicitly labeled datasets or assume consistent skill execution to enable learning and active control of individual behaviors, which limits their applicability. In this work, we propose a cooperative adversarial method for obtaining single versatile policies with controllable skill sets from unlabeled datasets containing diverse state transition patterns by maximizing their discriminability. Moreover, we show that by utilizing unsupervised skill discovery in the generative adversarial imitation learning framework, novel and useful skills emerge with successful task fulfiLLMent. Finally, the obtained versatile policies are tested on an agile quadruped robot called Solo 8 and present faithful replications of diverse skills encoded in the demonstrations.

• The paper introduces the CASSI framework, which uses a cooperative adversarial and self-supervised method to extract diverse robotic skills from unlabeled motion data.
• It combines generative adversarial imitation learning with unsupervised skill discovery to robustly mimic complex motion patterns and outperform baseline models.
• Experimental results on the Solo 8 robot demonstrate scalable, autonomous skill acquisition, highlighting potential for advanced multitask robotic applications.

Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions

The paper authored by Li et al. presents a cooperative adversarial framework termed Cooperative Adversarial Self-supervised Skill Imitation (CASSI). The core contribution of CASSI lies in its ability to extract and learn diverse, high-level skills from datasets of unlabeled robotic motion data. The research addresses the challenge of achieving versatile skill control in robotic systems, particularly through the lens of generative adversarial imitation learning (GAIL) enhanced by unsupervised skill discovery methods.

Overview of Methodology

The methodology integrates multiple advanced techniques in a structured manner, leveraging both adversarial and unsupervised learning frameworks. The system is designed to robustly and autonomously differentiate and control individual skills from complex and unlabeled motion data:

1. Generative Adversarial Imitation Learning: The learning model employs a specifically designed discriminator within a GAIL framework. The discriminator distinguishes between the generated motions from the robotic policy and the diverse state transitions present in the reference datasets. This incentivizes the policy to closely mimic the observed data distribution.
2. Unsupervised Skill Discovery: Unsupervised techniques are employed to discover and differentiate latent skills without task-specific labels. Vital to this is the optimization of mutual information between policy-conditioned latent variables and motion patterns extracted from robot states. This process incorporates a discriminator ensemble to overcome exploration challenges by addressing the problems of epistemic uncertainty and promoting exploration in novel state spaces.
3. Skill Discriminator Evaluation: The framework includes a built-in skill discriminator which undergoes training against ground-truth labels in a supervised manner post-initial unsupervised phase. This aids in effectively assessing the skill fidelity and diversity compared to approaches like spectral clustering, which are less effective in distinguishing concise skill types from brief sub-trajectory data.

Results and Analysis

The experimental results validate the proposed approach on the Solo 8 robot, demonstrating significant improvements in skill-fidelity over baselines like AMP without skill rewards. The policy's ability to generalize complex motion patterns, such as "crawl," "walk," and "trot," underscores the merit of unsupervised skill discovery in a GAIL context. By achieving notable scoring in terms of skill diversity and fidelity metrics, CASSI effectively surpasses methodologies that rely solely on labeled or pre-sorted datasets. The experiment involving oracle classifiers illustrates how well the extracted skills align with actual labeled reference data, ensuring a broad and distinct range of robotic behaviors.

Implications and Future Directions

The findings of this research have pronounced implications in the field of robotics, particularly for users in dynamic environments where manual data labeling is infeasible. The approach fosters scalable and autonomous acquisition of skills, potentially broadening the application and effectiveness of robotic learning systems across various domains where diverse unsupervised datasets are available. The research suggests that applying this approach alongside specific task objectives could refine and enhance results, providing an avenue for robust multitask capabilities in robotic systems.

Future work could involve scaling this approach to more complex robotic systems and environments, possibly incorporating additional sensing modalities to improve the discriminative capabilities of the learned models. Further exploration into optimizing the skill discovery process and minimizing reliance on human-specified hyperparameters could also yield advancements in deploying these systems in real-world contexts.