# LERa: Replanning with Visual Feedback in Instruction Following

arXiv: https://arxiv.org/abs/2507.05135 · HTML: https://mlsanity.com/paper/2507.05135/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2025.

## Built on by (most cited first, 7 of 7)

- [Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding](https://mlsanity.com/paper/2508.09032/) (2025)
- [Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation](https://mlsanity.com/paper/2508.15663/) (2025)
- [LookPlanGraph: Embodied Instruction Following Method with VLM Graph Augmentation](https://mlsanity.com/paper/2512.21243/) (2025)
- [Agentic Self-Evolutionary Replanning for Embodied Navigation](https://mlsanity.com/paper/2603.02772/) (2026)
- [Sequential Planning via Anchored Robotic Keypoints](https://mlsanity.com/paper/2606.30613/) (2026)
- [MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation](https://mlsanity.com/paper/2602.16898/) (2026)
- [HELP: Hierarchical Embodied Language Planner for Household Tasks](https://mlsanity.com/paper/2512.21723/) (2025)

## Abstract (arXiv)

Large Language Models are increasingly used in robotics for task planning, but their reliance on textual inputs limits their adaptability to real-world changes and failures. To address these challenges, we propose LERa - Look, Explain, Replan - a Visual Language Model-based replanning approach that utilizes visual feedback. Unlike existing methods, LERa requires only a raw RGB image, a natural language instruction, an initial task plan, and failure detection - without additional information such as object detection or predefined conditions that may be unavailable in a given scenario. The replanning process consists of three steps: (i) Look - where LERa generates a scene description and identifies errors; (ii) Explain - where it provides corrective guidance; and (iii) Replan - where it modifies the plan accordingly. LERa is adaptable to various agent architectures and can handle errors from both dynamic scene changes and task execution failures. We evaluate LERa on the newly introduced ALFRED-ChaOS and VirtualHome-ChaOS datasets, achieving a 40% improvement over baselines in dynamic environments. In tabletop manipulation tasks with a predefined probability of task failure within the PyBullet simulator, LERa improves success rates by up to 67%. Further experiments, including real-world trials with a tabletop manipulator robot, confirm LERa's effectiveness in replanning. We demonstrate that LERa is a robust and adaptable solution for error-aware task execution in robotics. The project page is available at https://lera-robo.github.io.