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Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

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arXiv:2605. 30503v1 Announce Type: cross Abstract: Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs.

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Authors: Vittorio Giammarino, Anastasios Manganaris, Ahmed H. Qureshi
Released: 2026-06-01 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: under, control. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.30503

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