arXiv (All)
open access
Robots Influencing Humans to Reveal their Goals during Collaboration and Competition
We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-con
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ARS: Agentic Reward System for Robot Learning#1128753
- Keep It in Sight: Reward Function Compression Through Goal Visibility#469716
- Intrinsic Robot Rewarding: Reusing VLA Representations for Autonomous Evaluation and Policy Improvement#1123506
- Intrinsic Robot Rewarding: Reusing VLA Representations for Autonomous Evaluation and Policy Improvement#919404
- A dynamic theory of multi-agent goal pursuit in continuous time and space#361063
Record · ID 972580
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive