DC-Ada: Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams
Researchers have introduced DC-Ada, a decentralized adaptation strategy focused solely on rewards for diverse multi-robot teams. This method maintains a frozen pretrained shared policy while adjusting compact observation transformations for each robot to align varied sensing into a consistent inference framework. DC-Ada operates without gradients and minimizes communication, employing a budgeted accept/reject random search with brief rollouts using common random numbers, all within a strict step budget. The effectiveness of this approach is tested against four baselines in a deterministic 2D multi-robot simulator, addressing scenarios in warehouse logistics, search and rescue, and collaborative mapping across four levels of heterogeneity (H0–H3). This research tackles the issue of controllers, which perform poorly when deployed on robots with absent or mismatched sensors.
Key facts
- DC-Ada is a reward-only decentralized adaptation method for heterogeneous multi-robot teams.
- It keeps a pretrained shared policy frozen and adapts compact per-robot observation transforms.
- DC-Ada is gradient-free and communication-minimal.
- It uses budgeted accept/reject random search with short common-random-number rollouts.
- Evaluation is done in a deterministic 2D multi-robot simulator.
- Simulator covers warehouse logistics, search and rescue, and collaborative mapping.
- Four heterogeneity regimes (H0–H3) are tested.
- The method addresses sensor mismatch in multi-robot systems.
Entities
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