New framework enables robots to learn new skills without forgetting old ones

Researchers have developed a new framework called Continual Policy Consolidation (CPC) to enable robots to learn new skills without forgetting previous ones. CPC uses a teacher-student model where independent teachers learn skills via reinforcement learning, and their behaviors are distilled into a central student policy. This approach separates skill acquisition from consolidation, treating the student's learning as a supervised task. The student policy utilizes an expandable Transformer-based mixture-of-experts architecture and prioritized trajectory replay to balance stability and plasticity as more tasks are added. AI

IMPACT This research could lead to more adaptable and capable robots that can continuously learn and retain a wide range of skills over time.

RANK_REASON The cluster contains a research paper detailing a new method for lifelong robot learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Continual Policy Consolidation
• Expandable Experts
• mixture of experts
• Prioritized Experience Replay
• Prioritized Trajectory Replay
• reinforcement learning
• Transformer++
• Yuxuan Li

• paper
• model release

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