🤖 AI Summary
This work addresses the performance degradation in multimodal imitation learning caused by missing visual or language inputs. The authors propose an end-to-end framework that operates without retraining, integrating a reinforcement learning–guided retrieval mechanism—based on Proximal Policy Optimization (PPO) and breadth-first search—to select the most relevant demonstrations from an expert dataset. Action signals are fused via soft cross-attention, and when modalities are missing, dedicated retrieval strategies combined with an embedding imputation head dynamically reconstruct the absent information. Experiments on three LIBERO benchmarks demonstrate that the proposed method significantly outperforms existing imitation learning approaches and maintains high robustness and performance even under sensor failure conditions.
📝 Abstract
Robotic systems perceive the world through multiple input modalities -- including visual camera streams and natural language instructions -- and must select appropriate actions based on these signals. However, assuming the permanent availability of all input devices is unrealistic, as sensors may fail, become occluded, or drop out entirely during deployment. Robust handling of such missing-modality scenarios is therefore essential for real-world robot operation. This paper introduces RL4IL, a reinforcement learning guided method for imitation learning that selects the most suitable action for a given observation by identifying the most relevant expert demonstrations from a training library. A reinforcement learning policy, trained via Proximal Policy Optimisation over Breadth-First Search candidate sets, ranks candidate demonstrations and a soft cross-attention fusion head aggregates their action signals to produce the final prediction. When a modality is missing at inference time, a dedicated per-modality RL retrieval policy identifies donor demonstrations from the training library, and a soft imputation head reconstructs the missing embedding via cross-attention over the top-ranked donors -- without requiring any retraining of the system. Experiments on three LIBERO benchmark suites demonstrate that RL4IL substantially outperforms state-of-the-art imitation learning methods under sensor dropout conditions, while requiring no policy network training. The code can be found at https://github.com/h-ismkhan/Reinforcement-Learning-via-kNN-for-Robotic-Learning-with-Missing-Camera