Mitigating Cross-Modal Distraction and Ensuring Geometric Feasibility via Affordance-Guided and Self-Consistent MLLMs for Task Planning in Instruction-Following Manipulation

• URL: http://arxiv.org/abs/2503.13055v2
• Date: Wed, 08 Oct 2025 03:30:23 GMT
• Title: Mitigating Cross-Modal Distraction and Ensuring Geometric Feasibility via Affordance-Guided and Self-Consistent MLLMs for Task Planning in Instruction-Following Manipulation
• Authors: Yu-Hong Shen, Chuan-Yu Wu, Yi-Ru Yang, Yen-Ling Tai, Yi-Ting Chen,
• Abstract summary: We introduce textbfQuARC (Quantity, Analysis, Relative positioning, Collision), a new benchmark based on a food preparation scenario.<n>We tackle two major limitations of current MLLMs: cross-modal distraction and geometric infeasibility.<n>Our method achieves a 76.7% success rate on the benchmark, significantly outperforming the ViLa baseline.
• Score: 5.903105418868711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the use of Multimodal Large Language Models (MLLMs) with in-context learning for closed-loop task planning in instruction-following manipulation. We identify four essential requirements for successful task planning: quantity estimation, reachability analysis, relative positioning, and collision avoidance. However, existing benchmarks fail to support holistic evaluation across all these aspects. To address this gap, we introduce \textbf{QuARC} (Quantity, Analysis, Relative positioning, Collision), a new benchmark based on a food preparation scenario that integrates all four challenges. Using QuARC, we reveal two major limitations of current MLLMs: cross-modal distraction and geometric infeasibility. To tackle these, we adapt Chain-of-Thought with Self-Consistency to mitigate reasoning loss from cross-modal distractions and incorporate an affordance predictor to guide planning based on geometric feasibility. Our comprehensive evaluation analyzes performance across multiple baselines and explains sources of improvement. Our method achieves a 76.7\% success rate on the benchmark, significantly outperforming the ViLa baseline (36.7\%), without requiring additional finetuning. Code and dataset are available at https://hcis-lab.github.io/Affordance-Guided-Self-Consistent-MLLM.

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