We’ve found that self-play allows simulated AIs to discover physical skills like tackling, ducking, faking, kicking, catching, and diving for the ball, without explicitly designing an environment with these skills in mind. Self-play ensures that the environment is always the right difficulty for an AI to improve.
arXiv:2606. 19419v1 Announce Type: cross Abstract: Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions.
By Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin, Justin Yu, Dantong Niu, Zirui Wang, Roei Herzig, Ken Goldberg, Yutong Bai, David M. Chan, Ion Stoica, Angjoo Kanazawa, Jiahui Lei, Haiwen Feng, Trevor Darrell
arXiv:2609.40137v1 Announce Type: cross
Abstract: 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...
By Seungeun Rho, Jeonghwan Kim, Xue Bin Peng, Sehoon Ha
arXiv:2608. 05558v1 Announce Type: cross Abstract: This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games.
By Sharon Temtsin, Christoph Bartneck
The paper analyzes Turing’s 1948 report "Intelligent Machinery" as a foundational source for later imitation games, highlighting key design concepts such as the possibility of machine errors, the exclusion of irrelevant physical traits, the role of a human judge, and Turing’s view that intellectual activity is largely search. It argues that limiting the human contestant to a weak chess player heightens the importance of intellectual search, making human behavior more comparable to machine behavior. This reframes the 1948 game as a human‑approximates‑machine scenario, suggesting that imitation games can probe when human intelligence becomes machine‑like under specific task constraints.
By Sharon Temtsin, Christoph Bartneck
arXiv:2608. 11246v1 Announce Type: new Abstract: The success of coding agents has established the harness as a paradigm: what an agent achieves depends not on the model alone, but on the infrastructure around it.
By Qi Wang, Tianyi Wang, Chengyang Li, Shikun Ban, Yurun Chen, Yizhong Ge, Jason Qin, Chengtai Li, Wentao Zhu