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Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model

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arXiv:2609.09213v1 Announce Type: new 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-c…

SourcearXiv RoboticsAuthor: Hao Li, Haofei Sun, Lin He
Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model
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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

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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)

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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

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supp/records/frame_relabeling/mtS_b0state_rot90.json.episodes.jsonl

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supp/records/frame_relabeling/mtS_c1state_rot90.json.episodes.jsonl

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supp/records/layout_shift/layE_c0_host_s0_t0_cm10.json.episodes.jsonl

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supp/records/layout_shift/layE_c2_relcoord_concat_s0_t0_cm5.json.episodes.jsonl

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supp/records/layout_shift/layE_f0_geometry_decay_s0_t4_cm0.json.episodes.jsonl

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supp/records/layout_shift/layE_f0_geometry_decay_s0_t4_cm5.json.episodes.jsonl

supp/records/layout_shift/layE_f0_shuffled_geometry_s0_t0_cm0.json.episodes.jsonl

supp/records/layout_shift/layE_f0_shuffled_geometry_s0_t0_cm10.json.episodes.jsonl

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supp/records/layout_shift/layE_f0_shuffled_geometry_s0_t4_cm0.json.episodes.jsonl

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supp/records/main_table/m3E_b1_clock_decay_s0_t4.json.episodes.jsonl

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supp/records/main_table/m3E_c1_coord_mlp_s0_t0.json.episodes.jsonl

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supp/records/split_manifest.json

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supp/scripts/analyze.py

supp/scripts/paired_stats.py

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  • arXiv:2609.09213v1 Announce Type: new Abstract: We study how physical-state inputs affect a 0.8B hybrid language model adapted for manipulation with 6.2M trainable parameters. Six…

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