49 accepted papers
Stereo depth estimation has drawn widespread attention from the robotics and vision community due to its broad applications such as 3D reconstruction. Recently, stereo matching foundation models have made significant progress by being trained on the large-scale datasets containing natural images. Ho…
In computer-assisted orthopedic surgery (CAOS), accurately registering sparse and partial intraoperative point sets with a complete preoperative model remains highly challenging due to limited overlap, extreme sparsity, and point localisation noise. In this paper, we propose a novel end-to-end compl…
Legged robots are supposed to traverse complicated environments, which makes it challenging to design a model-based controller due to their functional complexity. Currently, using deep reinforcement learning to improve the adaptability of robots in complex scenarios has been a major research trend.
Spherical tensegrity structure has good dynamic stability, support strength and flexibility, and is widely used in the field of mobile robot research. Most of the tensegrity spherical robots deform themselves to make gravity work to realize the motion, but the deformation of both rods and ropes affe
3D point cloud registration is an essential problem in computer vision, robotics, surgical navigation and augmented reality. Accurate registration of partially overlapped intraoperative point clouds (e.g., femoral reconstruction) remains critical yet challenging in orthopedic navigation due to incom
Humanoid robots are vital tools for substituting humans in various operational scenarios. A sufficiently large stationary reachability is a key factor in ensuring their operational capability. To address this challenge, this paper proposes a whole-body reachability enhancing approach for humanoid ro
We present GOEN, an advanced navigation and path planning framework specifically engineered to tackle the complexities of dynamic and unstructured environments through real-time 3D pointcloud processing. Our approach integrates pointcloud downsampling, collision risk assessment, and obstacle endpoin
To enhance the efficiency of cooperative transportation by multiple mobile robots, we propose a transportation strategy based on multi-objective optimization of robot configurations. The system consists of multiple omnidirectional robots equipped with passively rotatable linkages, enabling the appli
In computer-assisted orthopedic surgery (CAOS), accurate point set registration is essential for enhancing surgical accuracy. However, the sparse and low-overlap nature of intraoperative point sets presents significant challenges for reliable registration. To deal with these challenges, we propose a
In this paper, we present a novel curve-to-surface registration method, termed Bi-directional Hybrid Mixture Model Registration based on Dual-constrained Tangent and Normal Vectors (BiHMM-DTN), where two different tangent vectors at the intraoperative point are simultaneously used with the normal ve
Robust and accurate 2D/3D registration, which aligns preoperative models with intraoperative images of the same anatomy, is crucial for successful interventional navigation. To mitigate the challenge of a limited field of view in single-image intraoperative scenarios, multi-view 2D/3D registration i
Compared with traditional in-pipe robots, tensegrity robots have exhibited many advantages such as light-weight, compliant, collapsible, low-cost, and rapidly manufacturable characteristics. However, published tensegrity in-pipe robots still have limited load capacity, because they rely on the stres
In this paper, we propose a novel unsupervised intraoperative liver deformation correction method, called Learning Coherent point drift Network (LCNet), for image-guided liver surgery (IGLS). We first estimate the correspondences between the preoperative and intraoperative point sets in the optimal
In this paper, we introduce Bidirectional Non-Overlapping Filtering Network (Bi-NOFNet), which registers the partial intraoperative point set with full preoperative point set for computer-assisted interventions (CAI). Our contributions are three-folds. First, Bi-NOFNet adopts customised feature extr…
In this paper, we introduce a novel normal-assisted learning-based rigid registration approach, i.e., Deep Bi-directional Hybrid Mixture Registration (DeepBHMR). Our approach utilises helpful normal vectors explicitly in both correspondence and transformation stages and formulates the optimization o
Quadruped robots are able to traverse most terrains on the earth using a wide variety of gaits, providing solutions for robots to operate in specialized environments. Although existing methods have achieved excellent performance in gait generation, they suffer from catastrophic forgetting and inabil
In this paper, we introduce a novel overlap-based bidirectional point set registration approach, i.e., Overlap-guided Bidirectional Hybrid Mixture Registration (OBHMR), which incorporates geometric information (i.e., normal vectors) in both the correspondence and transformation stages and formulates…
Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid…
Snap-fit recognition is an essential capability for industrial robots in manufacturing. The goal is to protect fragile parts by quickly detecting snap-fit signals in the assembly. In this letter, we propose a fast recognition method of snap-fit for industrial robots. A snap-fit dataset generation st
This letter presents a control framework for legged robots that enables self-perception and resistance to external disturbances. First, a novel proprioceptive-based disturbance estimator is proposed. Compared with other disturbance estimators, this estimator possesses notable advantages in terms of
Most current in-pipe robots are usually designed for pipes of a specific size. In this letter, we propose a novel inchworm-like in-pipe robot based on the concept of tensegrity for moving in pipes with varying diameters. Firstly, a tensegrity-based robotic module capable of two kinds of shape change
This paper presents a novel concept to develop robots capable of crawling in tubular environments, inspired by the movement of earthworms and the biological musculoskeletal systems in nature. A tensegrity structures-based robotic module with shape changeability actuated by only one linear actuator i…
The mechanisms of locomotion in mammals have been extensively studied and inspire the related researches on designing the control architectures for the legged robots. Reinforcement learning (RL) is a promising approach allowing robots to automatically learn locomotion policies. However, careful rewa
A novel legged robot with 6 limbs driven by 20 proprioceptive motors, named SDUHex, is proposed in this letter. The limbs located at the middle of the robot can work as manipulators or locomotors. The topology design of the multi-mode robot is introduced and the kinematics and dynamics of the robot
Multi-robot coarse-to-fine exploration in unknown environments makes great sense in many application fields like search and rescue. For different stages of the task, robots need to extract information from the environment discriminately, which can improve their decision-making capability. To this en
This study focuses on localization and mapping for UGVs when they are deployed in environments with non-level roads. In these scenarios, the vehicles need to travel through flat but not necessarily level grounds, i.e., ascent or descent, which may cause drifts of the robot pose and distortion of the…
In an environment with limited space and dense goal configuration, the path of robot team is forced to coincide without much adjustment space, which is a challenge for multi-robot collaborative path planning. In this work, a novel Optimal Path and Timetable Planning (OPTP) method is proposed. The OP
There is an emerging research interest in addressing the online 3D bin packing problem (3D-BPP), which has a wide range of applications in logistics industry. However, neither heuristic methods nor those based on deep reinforcement learning (DRL) outperform human packers in real logistics scenarios.…
Quadruped locomotion is a challenging task for learning-based algorithms. It requires tedious manual tuning and is difficult to deploy in reality due to the reality gap. In this paper, we propose a quadruped robot learning system for agile locomotion which does not require any pre-training and works…
This paper focuses on developing a real-time and flexible velocity estimation approach for serial revolute manipulator using only one inertial measurement unit (IMU) mounted on each link side of the manipulator. Particularly, the proposed approach has no requirement for the installation position and…
In this paper, we propose a novel deep reinforcement learning (DRL) system for the autonomous navigation of mobile robots that consists of three modules: map navigation, multi-view perception and multi-branch control. Our DRL system takes as the input a routed map provided by a global planner and th…
The bones, muscles, tendons and connective tissues form a continuous tension network throughout human body. This heterogeneous mixture presents the characteristics of tensegrity, providing the body with structurally integrity, stability and flexibility. Inspired by this, this paper proposes a novel…
In a goal-conditioned grasping task, a robot is asked to grasp the objects designated by a user. Existing methods for goal-conditioned grasping either can only handle relatively simple scenes or require extra user annotations. This letter proposes an autonomous method to enable the grasping of targe
A method for planning and controlling the somersault motion of a quadruped robot is proposed in this paper. The method divides the somersault motion into 5 stages according to intuitive understanding. Based on the simplified dynamic model, the linear programming method is used to obtain the maximum…
Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the o…
In this letter, we present a novel navigation system of unmanned ground vehicle (UGV) for local path planning based on deep reinforcement learning. The navigation system decouples perception from control and takes advantage of multi-modal perception for a reliable online interaction with the surroun
Compared with torque-control techniques, a position-controlled quadruped robot is lower cost, easier to build, and more direct to drive. However, the stiff actuation of position-controlled actuators makes it difficult for the quadruped to achieve dynamically stable locomotion. This paper presents an…
In this paper, we propose an autonomous method for robot navigation based on a multi-camera setup that takes advantage of a wide field of view. A new multi-task network is designed for handling the visual information supplied by the left, central and right cameras to find the passable area, detect t…
A floating base system is inevitably to contact the environment while it is moving. This paper explores the contact force estimation and regulation algorithm for a position-controlled floating base system without joint torque information. First, the joint space dynamic model of the system is present…
Imitation learning enables robots to learn a task by simply watching the demonstration of the task. Current imitation learning methods usually require the learner and demonstrator to occur in the same context. This limits their scalability to practical applications. In this paper, we propose a more…
Integrated robotic arm system should contain both grasp and place actions. However, most grasping methods focus more on how to grasp objects, while ignoring the placement of the grasped objects, which limits their applications in various industrial environments. In this research, we propose a model-…
We present an imitation learning method for autonomous drone patrolling based only on raw videos. Different from previous methods, we propose to let the drone learn patrolling in the air by observing and imitating how a human navigator does it on the ground. The observation process enables the autom…
A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different t…
Learning actions from human demonstration is an emerging trend for designing intelligent robotic systems, which can be referred as video to command. The performance of such approach highly relies on the quality of video captioning. However, the general video captioning methods focus more on the unde…
This paper presents the dynamic modelling and motion planning method for a quadruped robot that uses its legs for nonprehensile manipulation as well as locomotion. Three different working modes named Drive Mode, Inchworm Mode and Scoot Mode are proposed to enable the robot to move forward together w…
This paper presents the dynamic modelling and motion planning method for a quadruped robot that uses its legs for nonprehensile manipulation as well as locomotion. Three different working modes named Drive Mode, Inchworm Mode and Scoot Mode are proposed to enable the robot to move forward together w
This paper explores the control strategy for a multi-limb robot nonprehensilely pushing an object to slide on the floor. The robot's limb distals perform point contacts with the object and the floor. The contact velocity constraint and force constraint are proposed to prevent separation and restrict…
A novel gait planning and control framework was developed for quadruped locomotion of a robot. It modeled the quadruped robot as two bipeds carrying the body from the front and rear ends. We first mapped the relationship between the joint torques of support legs and the torso forces of the bipedal s…
Object tracking is an important capability for robots tasked with interacting with humans and the environment, and it enables robots to manipulate objects. In object tracking, selecting samples to learn a robust and efficient appearance model is a challenging task. Model learning determines both the…