{"ID":2864677,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23244","arxiv_id":"2509.23244","title":"Online Dynamic Goal Recognition in Gym Environments","abstract":"Goal Recognition (GR) is the task of inferring an agent's intended goal from partial observations of its behavior, typically in an online and one-shot setting. Despite recent advances in model-free GR, particularly in applications such as human-robot interaction, surveillance, and assistive systems, the field remains fragmented due to inconsistencies in benchmarks, domains, and evaluation protocols. To address this, we introduce gr-libs (https://github.com/MatanShamir1/gr_libs) and gr-envs (https://github.com/MatanShamir1/gr_envs), two complementary open-source frameworks that support the development, evaluation, and comparison of GR algorithms in Gym-compatible environments. gr-libs includes modular implementations of MDP-based GR baselines, diagnostic tools, and evaluation utilities. gr-envs provides a curated suite of environments adapted for dynamic and goal-directed behavior, along with wrappers that ensure compatibility with standard reinforcement learning toolkits. Together, these libraries offer a standardized, extensible, and reproducible platform for advancing GR research. Both packages are open-source and available on GitHub and PyPI.","short_abstract":"Goal Recognition (GR) is the task of inferring an agent's intended goal from partial observations of its behavior, typically in an online and one-shot setting. Despite recent advances in model-free GR, particularly in applications such as human-robot interaction, surveillance, and assistive systems, the field remains f...","url_abs":"https://arxiv.org/abs/2509.23244","url_pdf":"https://arxiv.org/pdf/2509.23244v1","authors":"[\"Shamir Matan\",\"Elhadad Osher\",\"Nageris Ben\",\"Mirsky Reuth\"]","published":"2025-09-27T10:50:53Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":609185,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864677,"paper_url":"https://arxiv.org/abs/2509.23244","paper_title":"Online Dynamic Goal Recognition in Gym Environments","repo_url":"https://github.com/MatanShamir1/gr_libs","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":609186,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864677,"paper_url":"https://arxiv.org/abs/2509.23244","paper_title":"Online Dynamic Goal Recognition in Gym Environments","repo_url":"https://github.com/MatanShamir1/gr_envs","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}