DC-Ada: Reward-Only Decentralized Observation-Interface Adaptation for Heterogeneous Multi-Robot Teams

1. 1Pretrain a shared policy on a nominal sensing setup
2. 2Freeze the shared policy and deploy it on heterogeneous robots with different observation interfaces
3. 3Use reward-only signals to adapt the observation interface for each robot
4. 4Update the adaptation module to improve the policy's performance on each robot

Robotics and AI engineers on a team developing multi-robot systems can benefit from DC-Ada, as it enables their systems to adapt to different sensing modalities and interfaces without requiring additional training data

💡 DC-Ada enables multi-robot teams to adapt to different sensing modalities and interfaces without requiring additional training data

🤖 DC-Ada: Decentralized adaptation for heterogeneous multi-robot teams using reward-only signals