Spatially Visual Perception for End-to-End Robotic Learning

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted camera observations remains a critical challenge. In this paper, we introduce a video-based spatial perception framework that leverages 3D spatial representations to address environmental variability, with a focus on handling lighting changes. Our approach integrates a novel image augmentation technique, AugBlender, with a state-of-the-art monocular depth estimation model trained on internet-scale data. Together, these components form a cohesive system designed to enhance robustness and adaptability in dynamic scenarios. Our results demonstrate that our approach significantly boosts the success rate across diverse camera exposures, where previous models experience performance collapse. Our findings highlight the potential of video-based spatial perception models in advancing robustness for end-to-end robotic learning, paving the way for scalable, low-cost solutions in embodied intelligence.

Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework.

MeetDot: Videoconferencing with Live Translation Captions

We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.

MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Training

We create a new task-oriented dialog platform (MEEP) where agents are given considerable freedom in terms of utterances and API calls, but are constrained to work within a push-button environment. We include facilities for collecting human-human dialog corpora, and for training automatic agents in an end-to-end fashion. We demonstrate MEEP with a dialog assistant that lets users specify trip destinations.