arXiv:2606. 28182v1 Announce Type: cross Abstract: Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years.
The paper introduces Predicting Intention of Partner (PIP), a method for zero‑shot coordination in partially observable settings. PIP employs a Joint‑view VAE to create a partner representation from local observations and uses Partner‑State Belief networks to infer hidden partner states from interaction history. Experiments on Burrito‑PO, Overcooked‑PO, and Melting Pot, along with human evaluation, show that PIP outperforms other methods and demonstrates effective coordination with unseen partners under occlusion.
arXiv:2610.17456v1 Announce Type: cross Abstract: Language models are often evaluated alone but increasingly deployed together, routed between, made to debate, ensembled, or collaboratively trained....
arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.
arXiv:2607. 27177v1 Announce Type: new Abstract: Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents.
Attacca is a new approach for training visual goal‑conditioned policies that can handle continuous long‑horizon task execution in embodied agents. It decouples goal images from the execution environment by sampling class‑compatible masked goals from other worlds, enabling the policy to ground targets in the current view via a target‑mask prediction head. The method also introduces behavioral‑phase conditioning to distinguish Search, Approach, and Interact stages, and achieves significant performance gains on Minecraft tasks, outperforming baselines by up to 7× on long‑horizon scenarios.
arXiv:2603.03768v2 Announce Type: replace-cross Abstract: Full-stack human-robot collaboration (HRC) can become brittle when replacing a planner, partner model, coordination policy, or controller cha...
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
EmbodiedSkills is a skill-based agent framework that enhances frozen vision‑language‑action (VLA) policies with world action models for consequence‑aware action selection. It defines reusable skills for observation, context encoding, candidate generation, consequence prediction, outcome evaluation, candidate comparison, and action execution, organized into five stages that support both continuous action chunks and pick‑and‑place primitives. The framework improves success rates on MetaWorld MT50, CLIPort, and CALVIN benchmarks by incorporating outcome predictions and lightweight comparators while keeping the core policy and observation encoders frozen.
A central capability of embodied agents is to accomplish complex objectives through sequences of interdependent tasks. Yet existing visual goal-conditioned policies underlying these agents are typical...
arXiv:2610.19215v1 Announce Type: new Abstract: Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents...