New Framework Enables Quadruped Robot Teams to Learn Coordination Skills Continuously
Researchers have developed Conquer, a framework that allows teams of quadruped robots to learn new coordination tasks sequentially while retaining previously learned skills without forgetting. The approach uses a semantic skill library that retrieves, adapts, and updates coordination strategies based on task requirements and team composition. This advancement addresses a key challenge in multi-robot systems: enabling continuous learning across varying tasks without degrading performance on earlier learned behaviors.
A new framework called Conquer enables multi-quadruped robot teams to coordinate on tasks in a continual learning setting, where new tasks arrive sequentially and robots must acquire new skills while preserving old ones. The system uses a semantic skill-library approach that formulates coordination as a retrieve-adapt-update process, with a team-structured backbone called Self-Allies-Goal (SAG) that accommodates variable team sizes by modeling each robot's state, teammate context, and task goals. For each new task, the system constructs a semantic descriptor, retrieves a relevant skill from the library for adaptation, and then updates the library with trajectory-level semantic descriptors organized by semantic distance. Simulation experiments demonstrated a 95.6% average success rate with strong forward transfer and negligible catastrophic forgetting, while real-world tests on Unitree Go2 robot teams validated practical deployment feasibility.
What's missing
The paper does not discuss computational overhead or real-time performance requirements for the retrieve-adapt-update cycle on physical robots, nor does it compare performance against alternative continual learning approaches for multi-agent systems. The limitations of the semantic descriptor design and potential failure modes when task distributions diverge significantly from training data are not explicitly addressed.
What different sources said
- arXiv cs.AICenter
Continual Quadruped Robots Coordination via Semantic Skill Discovery
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