12 accepted papers
Achieving 3D spatial awareness is crucial for surgical robotic manipulation, where precise and delicate operations are required. Existing methods either explicitly reconstruct the surgical scene prior to manipulation, or enhance multi-view features by adding wrist-mounted cameras to supplement the d
Transformer-based language models (LMs) have demonstrated remarkable performance on many natural language tasks, yet to what extent LMs possess the capability of generalizing to unseen logical rules remains not explored sufficiently. In classical logic category, abductive, deductive and inductive (A…
Large language models (LLMs) have achieved significant progress in mathematical reasoning, especially in elementary math. However, they remain indisposed on tackling complex questions at high-school or college levels, which put forward a more advanced requirement of mastering relevant mathematical t…
A major breakthrough in 3D reconstruction is the feedforward paradigm to generate pixel-wise 3D points or Gaussian primitives from sparse, unposed images. To further incorporate semantics while avoiding the significant memory and storage costs of high-dimensional semantic features, existing methods…
Recent advances in handling long sequences have unlocked new possibilities for long-context in-context learning (ICL). While existing research predominantly focuses on performance gains driven by additional in-context examples, the impact on the trustworthiness of generated responses remains underex…
In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. However, the pre-calibrated extrinsic parameters may gradually drift during operation, leading to a decrease in the accuracy of the perception system. It is challenging to ad
Visual Semantic Navigation (VSN) aims at navigating a robot to a given target object in a previously unseen scene. To tackle this task, the robot must learn a nimble navigation policy by utilizing spatial patterns and semantic co-occurrence relations among objects in the scene. Prevailing approaches…
Performing logical reasoning based on prior knowledge is a crucial human cognitive ability and has been a long-standing objective in the field of artificial intelligence. Large language models based on transformer architecture have been a common approach for logical reasoning over text. However, the…
Localization and mapping are critical tasks for various applications such as autonomous vehicles and robotics. The challenges posed by outdoor environments present particular complexities due to their unbounded characteristics. In this work, we present MM-Gaussian, a LiDAR-camera multimodal fusion s…
Social navigation is a key consideration for integrating robots into human environments. Concurrently, it imposes heightened requisites: tasks must not only be executed succesfully without collisions, but also adhere to principles encompassing comprehensibility, courtesy, social compliance, comprehe…
Socially aware Object Goal Navigation (ObjectNav) requires robots to navigate to objects with specific semantic categories while understanding complex human social awareness and semantic co-occurrence relations among objects. Existing solutions usually achieve scene representation by mapping these c
In this paper, we investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. Through an analysis of LLMs’ internal factual recall at each reasoning step via Knowledge Neurons, we reveal that LLMs fail…