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Online Dynamic Goal Recognition in Gym Environments

arXiv 2509.23244 ↗First posted 27 Sep 2025cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 27 Sep 2025.

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8 of 35 references.

References · 8

  1. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
  2. 2019Landmark-Based Approaches for Goal Recognition as Planningcited by 21
  3. 2020Interpretable Sequence Classification via Discrete Optimizationcited by 19
  4. 2017Heuristic Online Goal Recognition in Continuous Domainscited by 11
  5. 2022Goal Recognition as Reinforcement Learningcited by 9
  6. 2022Goal Recognition as a Deep Learning Task: the GRNet Approachcited by 9
  7. 2024ODGR: Online Dynamic Goal Recognitioncited by 5
  8. 2024Goal Recognition using Actor-Critic Optimizationcited by 5

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  1. 2025General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning
  2. 2025Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learning
  3. 2024GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy
  4. 2025Dual Goal Representations
  5. 2023GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning
  6. 2024MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning
  7. 2026GRAIL: Goal Recognition Alignment through Imitation Learning
  8. 2026Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning