arXiv (All)
open access
Game-Guided Skill Discovery through Self-Play for Playable Agent Control
We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans. Playable skills provide a compact abstraction for controlling embodied agents through a small set of learned behaviors rather than low-level actions.
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
- Game-Guided Skill Discovery through Self-Play for Playable Agent Control#1136833
- M1 a-tDCS does not acutely enhance motor skill acquisition of a dexterous, timing-based videogame task in adults.#292245
- Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?#985148
- SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-Based Humanoid Control#927320
- ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning#1155252
Record · ID 1154404
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