Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
For embodied reinforcement learning (RL) agents interacting with the environment, it is desirable to have rapid policy adaptation to unseen visual observations, but achieving zero-shot adaptation capability is considered as a challenging problem in the RL context. To address the problem, we present a novel contrastive prompt ensemble (ConPE) framework which utilizes a pretrained vision-language model and a set of visual prompts, thus enabling efficient policy learning and adaptation upon a wide range of environmental and physical changes encountered by embodied agents. Specifically, we devise a guided-attention-based ensemble approach with multiple visual prompts on the vision-language model to construct robust state representations. Each prompt is contrastively learned in terms of an individual domain factor that significantly affects the agent's egocentric perception and observation. For a given task, the attention-based ensemble and policy are jointly learned so that the resulting state representations not only generalize to various domains but are also optimized for learning the task. Through experiments, we show that ConPE outperforms other state-of-the-art algorithms for several embodied agent tasks including navigation in AI2THOR, manipulation in egocentric-Metaworld, and autonomous driving in CARLA, while also improving the sample efficiency of policy learning and adaptation.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingLanguage ModelingLanguage ModellingReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning Adaptive Cross-Embodiment Visuomotor Policy with Contrastive Prompt Orchestration
Learning adaptive visuomotor policies for embodied agents remains a formidable challenge, particularly when facing cross-embodiment variations such as diverse sensor configurations and dynamic properties. Conventional le…
Contrastive LearningEmbodied CoT Distillation From LLM To Off-the-shelf Agents
We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited, off-the-shelf devices. We present DeDer,…
Decision MakingIn-Context LearningLanguage ModelingLanguage Modelling+1GenRL: Multimodal-foundation world models for generalization in embodied agents
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task…
BenchmarkingReinforcement Learning (RL)Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity s…
Reinforcement LearningDomain AdaptationVideo GenerationLLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble
Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. In this work, rather than using LLMs directly as agents, we explore their use as tools…
Offline RLReinforcement Learning (RL)