Continual Quadruped Robots Coordination via Semantic Skill Discovery
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Existing methods for multi-quadruped manipulation typically focus on predefined or closed task families, often relying on multi-agent reinforcement learning (MARL) to train task-specific coordination policies. However, such methods struggle in open-ended continual learning settings, where tasks arrive sequentially and robots are expected to acquire new coordination skills while reusing previously learned ones without catastrophic forgetting. To address this challenge, we propose Conquer, a semantic skill-library fra...
| Authors | Daoqing Wang, Yuchen Xiao, Weixuan Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 June 2026 |
| Last updated at source | 10 June 2026 |
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