19 accepted papers
This letter presents a novel continuum robot designed to overcome the limitations of existing winding-based grasping methods. The robot is based on a conical spiral curve design and consists of multiple 2-degree-of-freedom flexible joint, enabling lateral helical gripping of irregular objects. A str
Robot guide dogs offer navigation assistance that will greatly expand the independent mobility of the visually impaired, but their effective use requires subtle human-robot coordination that is difficult for users to learn from generic verbal instructions. To tackle the challenge, we present CANINE,…
The dominant paradigm for training Large Vision-Language Models (LVLMs) in navigation relies on imitating expert trajectories. This approach reduces the complex navigation task to a sequence-to-sequence replication of a single correct path, fundamentally limiting the agent's ability to explore and g…
From serving a cup of coffee to positioning mechanical parts during assembly, stable object placement is a crucial skill for future robots. It becomes particularly challenging under geometric uncertainties, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xl
From stacking a tower of blocks to serving a cup of coffee, stable object placement is a crucial skill for future robots. It becomes particularly challenging under geometric uncertainties, e.g., when the object pose or shape is not known accurately. This work leverages a differentiable simulation mo…
Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local editing targets with the full-image context, leading to unintended modifications in non-target regions. In this paper, we
Generative world models have become essential data engines for autonomous driving, yet most focus on videos or occupancy grids and overlook the unique challenges of LiDAR. Extending LiDAR generation to dynamic 4D modeling requires addressing controllability, temporal coherence, and standardized eval
Modeling dynamic 3D environments from LiDAR sequences is central to building reliable 4D worlds for autonomous driving and embodied AI. Existing generative frameworks, however, often treat all spatial regions uniformly, overlooking the varying uncertainty across real-world scenes. This uniform gener
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional generation with poor controllability or adopt text-guided synthesis, which lacks fine-grained spatial control. Leveragin…
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey ph
Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant challenges due to inadequate alignment for fine-grained knowledge, which restricts their ability to accurately capture lo
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maint…
Imagine a child sketching the Eiffel Tower and asking a robot to bring it to life. Today’s robot manipulation systems can’t act on such sketches directly—they require precise 3D block poses as goals, which in turn demand structural analysis and expert tools like CAD. We present *StackItUp*, a system…
3D instance segmentation is a fundamental and critical task for enabling robots to operate effectively in unstructured 3D environments. In order to address the challenges posed by the high demand for large-scale annotated data and the limited availability of such data in the context of 3D instance s…
Deformable object manipulation (DOM) is a long-standing challenge in robotics and has attracted significant interest recently. This paper presents DaXBench, a differentiable simulation framework for DOM. While existing work often focuses on a specific type of deformable objects, DaXBench supports fl…
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most bench…
Knowledge and skills can transfer from human teachers to human students. However, such direct transfer is often not scalable for physical tasks, as they require one-to-one interaction, and human teachers are not available in sufficient numbers. Machine learning enables robots to become experts and…
"Current state-of-the-art semi-supervised object detection methods (SSOD) typically adopt the teacher-student framework featured with pseudo labeling and Exponential Moving Average (EMA). Although the performance is desirable, many remaining issues still need to be resolved, for example: (1) the tea…
Prioritized multi-objective optimization has been widely used within the operational space inverse dynamics control framework. In this paper, we present a constrained prioritized multi-objective optimization-base control formulation that extends to impedance control, including the `simple' impedance…