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[Submitted on 6 Sep 2026] Title:Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model View a PDF of the paper titled Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model, by Hao Li and 2 other authors View PDF HTML (experimental) Abstract:We study how physical-state inputs affect a 0.8B hybrid language model adapted for manipulation with 6.2M trainable parameters. Six conditions are trained on three LIBERO-Spatial tasks and evaluated over three seeds and 540 held-out rollouts. Conditioning recurrent decay gates on geometric increments yields 28.9% success, compared with 36.7% when those increments are shuffled during training and 24.4% without explicit object/goal geometry. Both geometry policies receive correct inputs at evaluation. A token adapter using the same increments scores 27.8%; differences vary across seeds and remain inconclusive. Token-clock conditioning scores 11.1%, including one seed that fails to converge. In separate robustness tests, a state-only relative-coordinate policy retains 7/10 success under frame relabeling, whereas all four tested visual policies fall to at most 3/20 after a 5 cm object displacement. These results show no reliable advantage from training-time geometric alignment under this recipe and illustrate the gap between coordinate invariance and physical-layout generalization. Episode records, seed-level analyses, and figure-generation code accompany the paper. Comments: 7 pages, 3 figures. Includes ancillary data and analysis code Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.09213 [cs.RO] (or arXiv:2609.09213v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.09213 arXiv-issued DOI via DataCite Submission history From: Haofei Sun [view email] [v1] Sun, 6 Sep 2026 05:55:24 UTC (561 KB) Full-text links: Access Paper: View a PDF of the paper titled Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model, by Hao Li and 2 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): supp/README.md supp/SHA256SUMS supp/analysis.json supp/figures/architecture.pdf supp/figures/architecture.tex.txt supp/preregistered_ledger_ANONYMIZED.md supp/records/frame_relabeling/mtS_b0state_rot0.json.episodes.jsonl supp/records/frame_relabeling/mtS_b0state_rot45.json.episodes.jsonl 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supp/records/main_table/m3E_f0_shuffled_geometry_s1_t8.json.episodes.jsonl supp/records/main_table/m3E_f0_shuffled_geometry_s2_t0.json.episodes.jsonl supp/records/main_table/m3E_f0_shuffled_geometry_s2_t4.json.episodes.jsonl supp/records/main_table/m3E_f0_shuffled_geometry_s2_t8.json.episodes.jsonl supp/records/split_manifest.json supp/records/training_summaries.json supp/scripts/analyze.py supp/scripts/paired_stats.py (92 additional files not shown) Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... 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