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

2026-08-03

AI Summary

This work addresses two prevailing paradigms in robot learning—“weight internalization” and “code-based skill self-generation”—by clarifying their distinctions and evolutionary trajectories, and tackling open challenges such as ambiguous skill definitions, ill-defined self-improvement mechanisms, and cross-platform adaptability. The authors propose a unified taxonomic framework that articulates five distinct interpretations of “skills,” operationalizes self-improvement for the first time, and systematically evaluates six technical families: vision-language-action models, zero-shot program synthesis, closed-loop self-repair, persistent skill memory, reinforcement learning–based skill discovery, and large language model–driven skill repositories with evolutionary search. A review of 77 representative systems reveals that only a few—including ASPIRE, ENPIRE, and RoboClaw—achieve open-ended self-improvement loops, while highlighting critical gaps in current commercial skill markets regarding adaptability and safety validation.

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.

Problem

• robot learning
• skills
• self-improvement
• code-as-policy
• skill economy

Innovation

• code-as-policy
• self-improvement
• robot skill
• VLA models
• skill economy