Robotics agents often struggle to understand and follow the multi-modal prompts in complex manipulation scenes which are challenging to be sufficiently and accurately described by text alone. Moreover, for long-horizon manipulation tasks, the deviation from general instruction tends to accumulate if lack of intermediate guidance from high-level subgoals. For this, we consider can we generate subgoal images before act to enhance the instruction following in long-horizon manipulation with multi-modal prompts? Inspired by the great success of diffusion model in image generation tasks, we propose a novel hierarchical framework named as CoTDiffusion that incorporates diffusion model as a high-level planner to convert the general and multimodal prompts into coherent visual subgoal plans, which further guide the low-level policy model before action execution. We design a semantic alignment module that can anchor the progress of generated keyframes along a coherent generation chain, unlocking the chain-of-thought reasoning ability of diffusion model. Additionally, we propose bi-directional generation and frame concat mechanism to further enhance the fidelity of generated subgoal images and the accuracy of instruction following. The experiments cover various robotics manipulation scenarios including visual reasoning, visual rearrange, and visual constraints. CoTDiffusion achieves outstanding performance gain compared to the baselines without explicit subgoal generation, which proves that a subgoal image is worth a thousand words of instruction. The details and visualizations are available at https://cotdiffusion.github.io.

Robotic manipulation tasks often span over long horizons and encapsulate multiple subtasks with different skills. Learning policies directly from long-horizon demonstrations is challenging without intermediate keyframes guidance and corresponding skill annotations. Existing approaches for keyframe identification often struggle to offer reliable decomposition for low accuracy and fail to provide semantic relevance between keyframes and skills. For this, we propose a unified Keyframe Identifier and Skill Anotator (KISA) that utilizes pretrained visual-language representations for precise and interpretable decomposition of unlabeled demonstrations. Specifically, we develop a simple yet effective temporal enhancement module that enriches frame-level representations with expanded receptive fields to capture semantic dynamics at the video level. We further propose coarse contrastive learning and fine-grained monotonic encouragement to enhance the alignment between visual representations from keyframes and language representations from skills. The experimental results across three benchmarks demonstrate that KISA outperforms competitive baselines in terms of accuracy and interpretability of keyframe identification. Moreover, KISA exhibits robust generalization capabilities and the flexibility to incorporate various pretrained representations.

Long-horizon manipulation tasks with general instructions often implicitly encapsulate multiple sub-tasks, posing significant challenges in instruction following. While language planning is a common approach to decompose general instructions into stepwise sub-instructions, text-only guidance may lack expressiveness and lead to potential ambiguity. Considering that humans often imagine and visualize sub-instructions reasoning out before acting, the imagined subgoal images can provide more intuitive guidance and enhance the reliability of decomposition. Inspired by this, we propose **PERIA**(**PE**rceive, **R**eason, **I**magine, **A**ct), a novel framework that integrates holistic language planning and vision planning for long-horizon manipulation tasks with complex instructions, leveraging both logical and intuitive aspects of task decomposition. Specifically, we first perform a lightweight multimodal alignment on the encoding side to empower the MLLM to perceive visual details and language instructions. The MLLM is then jointly instruction-tuned with a pretrained image-editing model to unlock capabilities of simultaneous reasoning of language instructions and generation of imagined subgoals. Furthermore, we introduce a consistency alignment loss to encourage coherent subgoal images and align with their corresponding instructions, mitigating potential hallucinations and semantic conflicts between the two planning manners. Comprehensive evaluations across three task domains demonstrate that PERIA, benefiting from holistic language and vision planning, significantly outperforms competitive baselines in both instruction following accuracy and task success rate on complex manipulation tasks.