# RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback

arXiv: https://arxiv.org/abs/2303.07622 · HTML: https://mlsanity.com/paper/2303.07622/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention](https://mlsanity.com/paper/1812.04155/) (2018)
- [TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2109.05120/) (2021)
- [APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback](https://mlsanity.com/paper/2108.09801/) (2021)
- [Bayesian Imitation Learning for End-to-End Mobile Manipulation](https://mlsanity.com/paper/2202.07600/) (2022)
- [A Framework for Learning to Request Rich and Contextually Useful Information from Humans](https://mlsanity.com/paper/2110.08258/) (2021)
- [Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables](https://mlsanity.com/paper/1706.08495/) (2017)
- [Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation](https://mlsanity.com/paper/1810.00663/) (2018)
- [Can a Robot Trust You? A DRL-Based Approach to Trust-Driven Human-Guided Navigation](https://mlsanity.com/paper/2011.00554/) (2020)
- [Socially-Aware Robot Planning via Bandit Human Feedback](https://mlsanity.com/paper/2003.00658/) (2020)

## Built on by (most cited first, 3 of 3)

- [Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods](https://mlsanity.com/paper/2404.00282/) (2024)
- [DSADF: Thinking Fast and Slow for Decision Making](https://mlsanity.com/paper/2505.08189/) (2025)
- [LLM-Driven Intrinsic Motivation for Sparse Reward Reinforcement Learning](https://mlsanity.com/paper/2508.18420/) (2025)

## Abstract (arXiv)

Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures. To address this limitation, we propose a novel approach called RE-MOVE (REquest help and MOVE on) to adapt already trained policy to real-time changes in the environment without re-training via utilizing a language-based feedback. The proposed approach essentially boils down to addressing two main challenges of (1) when to ask for feedback and, if received, (2) how to incorporate feedback into trained policies. RE-MOVE incorporates an epistemic uncertainty-based framework to determine the optimal time to request instructions-based feedback. For the second challenge, we employ a zero-shot learning natural language processing (NLP) paradigm with efficient, prompt design and leverage state-of-the-art GPT-3.5, Llama-2 language models. To show the efficacy of the proposed approach, we performed extensive synthetic and real-world evaluations in several test-time dynamic navigation scenarios. Utilizing RE-MOVE result in up to 80% enhancement in the attainment of successful goals, coupled with a reduction of 13.50% in the normalized trajectory length, as compared to alternative approaches, particularly in demanding real-world environments with perceptual challenges.