Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation
We present \method{} (Cooperative Action--Response Belief Estimation), a framework for learning team-response beliefs from local history.
ProofPaper ↗
Key points
- In multi-robot manipulation, an action's value depends on how teammates respond.
- A predictive belief encoder represents the interaction and conditions a nominal action.
- Our analysis characterizes when response changes the preferred action and how prediction and source errors limit the recoverable gain.
- Together, these results show that CARE turns predictions of teammate adaptation into effective decentralized action selection.
Sources (1)
- [1]Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 04:17 AM
We present \method{} (Cooperative Action--Response Belief Estimation), a framework for learning team-response beliefs from local history.
In multi-robot manipulation, an action's value depends on how teammates respond.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 10, 2026EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment
- Oct 10, 2026LightVAE: Towards Compact and Efficient Video Autoencoders
- Oct 9, 2026Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction
- Oct 9, 2026Latent World Action Model for GUI Agents
- Oct 8, 2026openai/codex rust-v0.162.0: 0.162.0
- Oct 8, 2026Understanding Jev, the new model everyone is talking about