CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration

In embodied multi-agent collaboration, agents must coordinate their actions since completing one action may depend on unfinished work by another agent. Existing approaches use execution feedback to revise plans and generate recovery actions. However, resolving the immediate failure does not ensure completion of the affected subtask. Such corrections require verification before they can serve as successful training examples. We propose Counterfactual Contract Learning (CCL), which learns coordinated repair programs from alternatives verified against a contract specifying the local goal and previously satisfied conditions to preserve. Specifically, we initialize a repair model through supervised fine-tuning on verified programs. The model then generates a direct repair and samples alternatives from the same input. By replaying these programs from the same saved state under the fixed contract, we identify successful alternatives to failed direct repairs as training targets. During training, CCL aligns the paired programs and emphasizes the first differing action, while weaker supervision on subsequent actions teaches complete repairs. For evaluation, we construct PARTNR-FS, a benchmark of recorded failure states with verified repair examples. CCL improves strict recovery rates over Repair-SFT across four language models and, when integrated into CoELA and PCE, improves final object transport and transport progress on TDW-MAT.