Continual Quadruped Robots Coordination via Semantic Skill Discovery

Researchers present Conquer, a semantic skill-library framework enabling multi-quadruped robots to learn new coordination tasks sequentially without forgetting previously acquired skills. The system uses a variable-cardinality architecture and semantic descriptors to retrieve and adapt existing skills for new tasks, achieving 95.6% success rates in simulation and real-world validation on Unitree Go2 robots.

Conquer addresses a fundamental challenge in robotics: enabling teams of quadrupedal robots to continuously learn new coordination skills while maintaining previously learned behaviors. Traditional multi-agent reinforcement learning approaches train task-specific policies from scratch, failing to leverage prior knowledge and suffering from catastrophic forgetting when learning sequences of tasks. This research tackles the practical problem of deploying robot teams that must adapt to dynamic environments and emerging task requirements without complete retraining cycles.

The framework's innovation lies in its semantic skill-library approach, which abstracts task requirements into retrievable, adaptable components. The team-structured Self-Allies-Goal backbone is particularly significant because it handles variable team sizes—a critical real-world requirement where robots may join or leave teams. By organizing skills through semantic distance rather than explicit task categories, the system enables cross-task knowledge transfer and reduces the computational overhead of continuous learning.

For the robotics and autonomous systems industry, this development has substantial implications. Companies deploying multi-robot systems for warehousing, construction, or exploration could significantly reduce training time and costs by leveraging skill libraries rather than training new policies for each task variant. The successful real-world validation on commercial Unitree Go2 platforms demonstrates practical feasibility beyond simulation environments, suggesting near-term deployment potential.

Looking ahead, the key question involves scaling this approach to larger teams and more complex task distributions. The 95.6% success rate and negligible catastrophic forgetting metrics provide a strong foundation, but real-world deployment challenges around partial observability, communication failures, and heterogeneous robot capabilities remain important considerations for production systems.

• →Conquer enables quadruped robot teams to learn new coordination tasks sequentially without forgetting previously learned skills through semantic skill libraries.
• →The framework handles variable team sizes by explicitly modeling individual robot states, teammate context, and task goals in a unified architecture.
• →Real-world validation on Unitree Go2 robots demonstrates practical deployment feasibility beyond simulation environments.
• →The approach achieves 95.6% success rates with strong forward transfer and negligible catastrophic forgetting across task sequences.
• →Semantic descriptors organize learned skills by similarity, enabling efficient knowledge reuse across different multi-robot coordination tasks.