Towards Adaptive Environment Generation for Training Embodied Agents

2026-02-06

AI Summary

This work addresses the limited generalization of embodied agents in novel environments and the inefficiency of existing environment generation methods, which lack feedback mechanisms tied to agent performance. To overcome these limitations, the authors propose a closed-loop, adaptive environment generation framework that translates fine-grained performance evaluations into environmental modification signals, dynamically adjusting both the difficulty and type of challenges in training scenarios. By integrating controllable environment representations with procedural generation techniques, the framework synthesizes training environments tailored to the agent’s current learning needs. Experimental results demonstrate that this approach significantly improves training efficiency and enhances generalization performance in previously unseen environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Generation
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Embodied agents struggle to generalize to new environments, even when those environments share similar underlying structures to their training settings. Most current approaches to generating these training environments follow an open-loop paradigm, without considering the agent's current performance. While procedural generation methods can produce diverse scenes, diversity without feedback from the agent is inefficient. The generated environments may be trivially easy, providing limited learning signal. To address this, we present a proof-of-concept for closed-loop environment generation that adapts difficulty to the agent's current capabilities. Our system employs a controllable environment representation, extracts fine-grained performance feedback beyond binary success or failure, and implements a closed-loop adaptation mechanism that translates this feedback into environment modifications. This feedback-driven approach generates training environments that more challenging in the ways the agent needs to improve, enabling more efficient learning and better generalization to novel settings.

Problem

• embodied agents
• environment generation
• generalization
• adaptive difficulty
• training efficiency

Innovation

• closed-loop environment generation
• adaptive difficulty
• embodied agents
• controllable environment representation
• performance feedback