Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills

Hugging Face Daily Papers Papers
robot-learning survey code-as-policy skill-learning vla-models self-improvement

Summary

A survey paper organizing robot-learning techniques along an axis of frozen-weight policies (VLA models) versus agents that write their own executable skills as code, providing a taxonomy of self-improvement mechanisms and analyzing the emerging robot-skill economy.

Robot learning is splitting into two bets: policies that bake competence into frozen weights (vision-language-action, or VLA, models), and agents that write and refine their own executable skills as code. This survey organises the field around that axis of weights versus skills. Its central analytical contribution is a deep-dive that arranges code-as-policy methods by their degree of self-improvement, from zero-shot program synthesis, through closed-loop self-repair and persistent skill memory, to the sparsely populated cell in which execution feedback, skill memory, and evolutionary search combine into one open-ended loop; only a few very recent systems (for example ASPIRE, ENPIRE, and RoboClaw) occupy that cell. We map the complementary "skills" pole, from unsupervised reinforcement-learning skill discovery to large-language-model skill libraries, and show that the word "skill" is used in at least five distinct senses, of which only the code sense self-improves without gradient updates. We then connect the taxonomy to the emerging skill economy: commercial robot-skill marketplaces now distribute one-tap skills across robots but ship only static playback, which surfaces open problems of adaptation, cross-embodiment portability, provenance, safety verification, composition, and standardisation. This is a deliberately focused survey. Rather than cataloguing the field exhaustively, it examines 77 representative systems across six technique families through one taxonomy and a set of contrast tables, and it supplies operational definitions of the self-improvement mechanisms together with a statement of what each family cannot do.
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Paper page - Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills

Source: https://huggingface.co/papers/2608.01851

Abstract

Robotlearningissplittingintotwobets:policiesthatbakecompetenceintofrozenweights(vision-language-action,orVLA,models),andagentsthatwriteandrefinetheirownexecutableskillsascode.Thissurveyorganisesthefieldaroundthataxisofweightsversusskills.Itscentralanalyticalcontributionisadeep-divethatarrangescode-as-policymethodsbytheirdegreeofself-improvement,fromzero-shotprogramsynthesis,throughclosed-loopself-repairandpersistentskillmemory,tothesparselypopulatedcellinwhichexecutionfeedback,skillmemory,andevolutionarysearchcombineintooneopen-endedloop;onlyafewveryrecentsystems(forexampleASPIRE,ENPIRE,andRoboClaw)occupythatcell.Wemapthecomplementary“skills“pole,fromunsupervisedreinforcement-learningskilldiscoverytolarge-language-modelskilllibraries,andshowthattheword“skill“isusedinatleastfivedistinctsenses,ofwhichonlythecodesenseself-improveswithoutgradientupdates.Wethenconnectthetaxonomytotheemergingskilleconomy:commercialrobot-skillmarketplacesnowdistributeone-tapskillsacrossrobotsbutshiponlystaticplayback,whichsurfacesopenproblemsofadaptation,cross-embodimentportability,provenance,safetyverification,composition,andstandardisation.Thisisadeliberatelyfocusedsurvey.Ratherthancataloguingthefieldexhaustively,itexamines77representativesystemsacrosssixtechniquefamiliesthroughonetaxonomyandasetofcontrasttables,anditsuppliesoperationaldefinitionsoftheself-improvementmechanismstogetherwithastatementofwhateachfamilycannotdo.

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