  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.
