arxiv:2608.01851

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

Published on Aug 3

· Submitted by

Aman Chadha on Aug 7

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Authors:

Aman Chadha ,

Abstract

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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amanchadha

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This survey reframes modern robot learning around what actually ships—frozen policy weights versus executable skills—and introduces a five-rung taxonomy of code-as-policy systems based on increasingly powerful combinations of execution feedback, persistent memory, and program search, culminating in autonomous self-improving robot skill loops.

➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬-𝐯𝐬-𝐒𝐤𝐢𝐥𝐥𝐬 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤:

🧭 𝑾𝒆𝒊𝒈𝒉𝒕𝒔 𝒗𝒔. 𝑺𝒌𝒊𝒍𝒍𝒔 𝑻𝒂𝒙𝒐𝒏𝒐𝒎𝒚: Introduces a unified taxonomy spanning 77 core systems across six robot-learning families—code-as-policy, end-to-end VLA, reward synthesis, skill libraries, sim-to-real/transfer, and benchmarks—plus 225 landscape works. Its key architectural distinction is whether competence is encoded in frozen neural weights (e.g., VLA backbone + action head) or represented as inspectable, executable programs/skills that can be edited and recombined after deployment. The taxonomy on page 3 makes this decomposition explicit.

🔄 𝑭𝒊𝒗𝒆-𝑹𝒖𝒏𝒈 𝑺𝒆𝒍𝒇-𝑰𝒎𝒑𝒓𝒐𝒗𝒆𝒎𝒆𝒏𝒕 𝑳𝒂𝒅𝒅𝒆𝒓 (𝑭 + 𝑴 + 𝑺): The paper's main analytical novelty is decomposing code-as-policy agents by three operational mechanisms—Feedback (F) from execution, Memory (M) persisted across tasks, and Search (S) over multiple candidate programs—and arranging systems from zero-shot synthesis → closed-loop repair → skill-library accumulation → evolutionary search → full F+M+S self-improvement. Crucially, it distinguishes sequential debugging from genuine search and frozen model parameters from runtime memory, making “self-improvement” technically testable rather than a loose label.

🧠 𝑭𝒖𝒍𝒍 𝑺𝒆𝒍𝒇-𝑰𝒎𝒑𝒓𝒐𝒗𝒊𝒏𝒈 𝑹𝒐𝒃𝒐𝒕 𝑳𝒐𝒐𝒑 + 𝑺𝒌𝒊𝒍𝒍 𝑬𝒄𝒐𝒏𝒐𝒎𝒚: Identifies a sparsely populated frontier—represented by ASPIRE, ENPIRE, and RoboClaw—where an agent executes skills, obtains grounded traces/feedback, stores validated skills in persistent memory, and searches/mutates candidate programs, feeding accumulated competence into future tasks. The architecture is summarized in the page-11 diagram as Actor Agent → Execution Engine (F) → Skill Memory (M) → Evolutionary Search (S) with skills recursively returned to future tasks. The survey argues this is the missing adaptation layer between today's static robot-skill marketplaces and genuinely deployable, continually improving robot ecosystems.