CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation
World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision making.
ProofPaper ↗
Key points
- To analyze this issue, we formulate a Structural Causal Model (SCM) for action evaluation in WAMs. The SCM formalizes how action-induced and environment-driven changes jointly shape future evaluation, and reveals a backdoor path through which environmental evolution can also affect the future evaluation associated with a candidate action.
- Based on this analysis, we propose Causal Action-Gain World Action Model (CAG-WAM).
- For each candidate action, CAG-WAM predicts an action-conditioned future and an environmental future under the same current observation, and defines their evaluation difference as causal action gain (CAG) for characterizing the future change associated with the candidate action.
- During training, we incorporate CAG into Group Relative Policy Optimization (GRPO) to guide robot policy learning.
Sources (1)
- [1]CAG-WAM: Causal Action-Gain World Action Models for Robot ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:40 AM
World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision making.
To analyze this issue, we formulate a Structural Causal Model (SCM) for action evaluation in WAMs. The SCM formalizes how action-induced and environment-driven changes jointly shape future evaluation, and reveals a backdoor path through which environmental evolution can also affect the future evaluation associated with a candidate action.
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