17 accepted papers
Dexterous manipulation is essential for real-world robot autonomy, mirroring the central role of human hand coordination in daily activity. Humans rely on rich multimodal perception--vision, sound, and language-guided intent--to perform dexterous actions, motivating vision-based, language-conditione
This paper presents GSWorld, a robust, photo-realistic simulator for robotics manipulation that combines 3D Gaussian Splatting with physics engines. Our framework advocates ‘closing the loop’ of developing manipulation policies with reproducible evaluation of policies learned from real-robot data an…
Collecting demonstrations through human teleoperation is an effective approach for learning complex manipulation skills. However, challenges such as morphology gaps, control latency, and limited feedback make high-quality data collection costly and inefficient. In this paper, we introduce Neural Tel…
We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception. By integrating 3D Gaussian Splatting techniques with foundation models, M3 builds a multimodal memory capable of rende…
Generating large-scale demonstrations for dexterous manipulation remains a challenging problem, and various approaches have been proposed in recent years to address it. Among these, generative models have emerged as a promising paradigm, enabling the efficient generation of diverse and plausible dem…
This paper proposes to solve the problem of Vision-and-Language Navigation with legged robots, which not only provides a flexible way for humans to command but also allows the robot to navigate through more challenging and cluttered scenes. However, it is non-trivial to translate human language inst…
‘In-the-wild’ mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations; (2) be capable of long-horizon task execution in diverse environments; and (3) perform complex manipulation beyond pic
The ability for robots to perform efficient and zero-shot grasping of object parts is crucial for practical applications and is becoming prevalent with recent advances in Vision-Language Models (VLMs). To bridge the 2D-to-3D gap for representations to support such a capability, existing methods rely…
Foundation models possess strong capabilities in reasoning and memorizing across modalities. To further unleash the power of foundation models, we present FIND, a generalized interface for aligning foundation models' embeddings with unified image and dataset-level understanding spanning modality and…
"With the recent significant advancements in large multimodal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent efforts to enable LMMs to support grounding, their capabilities for grounding and chat are usually separate, and their c…
"This paper presents (), a general-purpose multimodal assistant trained using an end-to-end approach that systematically expands the capabilities of large multimodal models (LMMs). maintains a skill repository that contains a wide range of vision and vision-language pre-trained models (tools), and i…
"In this work, we introduce , an augmented image segmentation foundation for segmenting and recognizing anything at desired granularities. Compared to the foundational segmentation model SAM [?], our model has two unique advantages: (i) granularity-controllability in that the model can produce segme…
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object falling short of addressin…
In this work, we present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that learns from different segmentation and detection datasets. To bridge the gap of vocabulary and annotation granularity, we first introduce a pretrained text encoder to encode all the visual concepts…
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-l…
In this work, we present SEEM, a promotable and interactive model for segmenting everything everywhere all at once in an image. In SEEM, we propose a novel and versatile decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal interface that behave…
Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or…