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arXiv · 2510.24055

Language-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation

Xiucheng Zhang·Yang Jiang·Hongwei Qing·Jiashuo Bai
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Abstract

Perceptual ambiguity and task conflict limit multitask robotic manipulation via imitation learning. We propose a framework combining a Language-Conditioned Visual Representation (LCVR) module and a Language-conditioned Mixture-ofExperts Density Policy (LMoE-DP). LCVR resolves perceptual ambiguities by grounding visual features with language instructions, enabling differentiation between visually similar tasks. To mitigate task conflict, LMoE-DP uses a sparse expert architecture to specialize in distinct, multimodal action distributions, stabilized by gradient modulation. On real-robot benchmarks, LCVR boosts Action Chunking with Transformers (ACT) and Diffusion Policy (DP) success rates by 33.75% and 25%, respectively. The full framework achieves a 79% average success, outperforming the advanced baseline by 21%. Our work shows that combining semantic grounding and expert specialization enables robust, efficient multi-task manipulation

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Xiucheng Zhang, Yang Jiang, Hongwei Qing, Jiashuo Bai. 2025-10-28. Language-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation. https://arxiv.org/abs/2510.24055

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