18 accepted papers
Robotic planning tasks often involve diverse complexities, which make adaptive improvement through reflection particularly challenging. Existing LLM-based approaches typically rely on fixed routines, lacking the ability to adjust to task-specific complexity and often leading to redundant reflections…
Soft grippers conform to the shape and surface properties of the objects to be grasped, effectively avoiding damage to soft and fragile items. Despite the variety of existing soft gripper designs, their structures lack sufficient flexibility for effectively grasping slender objects or operating in n
Autonomous error correction is critical for domestic robots to achieve reliable execution of complex long-horizon tasks. Prior work has explored self-reflection in Large Language Models (LLMs) for task planning error correction; however, existing methods are constrained by inflexible self-reflection
Multi-Agent Path Finding (MAPF) represents a pivotal area of research within multi-agent systems. Existing algorithms typically discretize the movement space of agents into grid or topology, neglecting agents' geometric characteristics and kinematic constraints. This limitation hampers their applica
Recent planning methods based on Large Language Models typically employ the In-Context Learning paradigm. Complex long-horizon planning tasks require more context(including instructions and demonstrations) to guarantee that the generated plan can be executed correctly. However, in such conditions, L…
Multi-task learning has expanded the boundaries of robotic manipulation, enabling the execution of increasingly complex tasks. However, policies learned through reinforcement learning exhibit limited generalization and narrow distributions, which restrict their effectiveness in multi-task training.…
In this letter, we focus on online motion planning for manipulators in dynamic obstacle environments. An analytical geometry-based inverse kinematics solution for generalized types of 7-DoF anthropomorphic manipulators is presented to work as the basis of high-efficiency collision avoidance planning
Dense depth estimation is an important task for applications, such as object detection, 3-D reconstruction, etc. Stereo matching, as a popular method for dense depth estimation, has been faced with challenges when low textures, occlusions or domain gaps exist. Stereo-LiDAR fusion has recently become
Scene graph generation (SGG) methods have historically suffered from long-tail bias and slow inference speed. In this paper, we notice that humans can analyze relationships between objects relying solely on context descriptions,and this abstract cognitive process may be guided by experience. For exa…
Despite the giant leap made in object 6D pose estimation and robotic grasping under structured scenarios, most approaches depend heavily on the exact CAD models of target objects beforehand, thereby limiting their wide applications. To address this, we propose a novel knowledge-guided network - KGNe…
In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach co…
Semi-supervised domain generalization (SSDG) has recently emerged as a potential research topic. Compared to domain generalization, SSDG represents a realistic and challenging goal, which only requires a few labels from source domains. To tackle this problem, this work presents a novel pseudo-labeli…
In this paper, we present a framework to generate time-optimal trajectories for dynamic target pick-and-place tasks. We develop an optimization-based trajectory generation method for manipulators, which can conduct spatial-temporal deformation under user-defined requirements. We formulate the proble…
Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objec…
Panoptic segmentation is a challenging scene understanding task that unifies semantic segmentation and instance segmentation. Namely, each pixel of an image is assigned a semantic label and an instance id. Existing works have elaborated end-to-end panoptic segmentation networks and made great progre…
In this work, we present a visual-inertial-pressure (VIP) fusion method for underwater robot localization. Specifically, this letter focuses on the tightly-coupled fusion of pressure measurements into a visual inertial odometry (VIO) based on sliding window optimization. Previous works used to assoc
This work considers the control of underactuated bipedal walking, and a novel capturability-based control framework is presented. Compared with traditional approaches, the presented control method does not rely on the use of the Poincaré map, which may take significant computational cost. Firstly, a…
The motion of underwater robot is usually slow, which leads to large scale error easily introduced in the positioning method based on monocular Visual Inertial Odometry (VIO). To solve the above problem, we present a positioning method based on visual-pressure fusion in this letter. Firstly, it is p