Compact Task-Aligned Imitation Learning for Laboratory Automation

• URL: http://arxiv.org/abs/2603.01110v1
• Date: Sun, 01 Mar 2026 13:51:55 GMT
• Title: Compact Task-Aligned Imitation Learning for Laboratory Automation
• Abstract summary: We propose a compact imitation learning framework for laboratory automation using small foundation models.<n>TVF-DiT, aligns a self-supervised vision foundation model with a vision-language model through a compact adapter.<n>Experiments on three real-world laboratory tasks demonstrate an average success rate of 86.6%.
• Score: 3.1796322524432092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic laboratory automation has traditionally relied on carefully engineered motion pipelines and task-specific hardware interfaces, resulting in high design cost and limited flexibility. While recent imitation learning techniques can generate general robot behaviors, their large model sizes often require high-performance computational resources, limiting applicability in practical laboratory environments. In this study, we propose a compact imitation learning framework for laboratory automation using small foundation models. The proposed method, TVF-DiT, aligns a self-supervised vision foundation model with a vision-language model through a compact adapter, and integrates them with a Diffusion Transformer-based action expert. The entire model consists of fewer than 500M parameters, enabling inference on low-VRAM GPUs. Experiments on three real-world laboratory tasks - test tube cleaning, test tube arrangement, and powder transfer - demonstrate an average success rate of 86.6%, significantly outperforming alternative lightweight baselines. Furthermore, detailed task prompts improve vision-language alignment and task performance. These results indicate that small foundation models, when properly aligned and integrated with diffusion-based policy learning, can effectively support practical laboratory automation with limited computational resources.

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