The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-pla...
详细信息
ISBN:
(纸本)9781728196817
The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by using an instance segmentation method. However, human annotations to train these methods are unreliable and error-prone since foods are packed close together with unclear boundaries and visual similarity making separation of pieces difficult. To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models. Another concern is that foods are easily damaged during grasping. We address this by introducing two additional methods- a novel adaptive finger mechanism to passively retract when a collision occurs, and a method to filter grasps that are likely to cause damage to neighbouring pieces of food during a grasp. We demonstrate the effectiveness of the proposed method on several kinds of real foods.
The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive ro...
详细信息
ISBN:
(纸本)9798350358513;9798350358520
The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a pipeline exploring the perception and "intelligent disobedience" required by such a system. A dataset of two people moving in and out of view has been prepared to compare RGB-based and event-based multi-modal dynamic object detection using LiDAR data for 3D position localisation. Our analysis highlights challenges in accurate 3D localisation using 2D image-LiDAR fusion, indicating the need for further refinement. Compared to the performance of the frame-based detection algorithm utilised (YOLOv4), current cutting-edge event-based detection models appear limited to contextual scenarios, such as for automotive platforms. This is highlighted by weak precision and recall over varying confidence and Intersection over Union (IoU) thresholds when using frame-based detections as a ground truth. Therefore, we have publicly released this dataset to the community, containing RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses for the two people in the scene to fill a gap in the current landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms in the future: https://***/revel/.
The high speed and low energy cost are two conflicting objectives in the motion optimization of bio-inspired underwater robots, but playing a very important role. To this end, this paper proposes an optimization strat...
详细信息
ISBN:
(纸本)9798350323658
The high speed and low energy cost are two conflicting objectives in the motion optimization of bio-inspired underwater robots, but playing a very important role. To this end, this paper proposes an optimization strategy for swimming speed and power cost using an improved NSGA-II for a flexible robotic fish. A dynamic model involving flexible deformation is established for speed prediction with the hydrodynamic parameters identified. A back propagation (BP) neural network is applied to perform compensation of power cost prediction with the dynamic model's prediction as input. In particular, an NSGA-II-AMS method is developed to improve the efficiency of solving the two-objective optimization problem based on NSGA-II. Finally, extensive simulations and experimental results demonstrate the effectiveness of the proposed optimization strategy, which offers promising prospects for the flexible robotic fish performing aquatic tasks with different performance constraints.
The electromechanical servo system is usually implemented by three closed loops, with the inner loop being the current loop, the middle loop being the speed loop, and the outer loop being the position loop. The tradit...
详细信息
Robots "in-the-wild" encounter and must traverse widely varying terrain, ranging from solid ground to granular materials like sand to full liquids. Numerous approaches exist, including wheeled and legged rob...
详细信息
ISBN:
(纸本)9798350323658
Robots "in-the-wild" encounter and must traverse widely varying terrain, ranging from solid ground to granular materials like sand to full liquids. Numerous approaches exist, including wheeled and legged robots, each excelling in specific domains. Screw-based locomotion is a promising approach for multi-domain mobility, leveraged in exploratory robotic designs, including amphibious vehicles and snake robotics. However, unlike other forms of locomotion, there is limited exploration of the models, parameter effects, and efficiency for multi-terrain Archimedes screw locomotion. In this work, we present work towards this missing component in understanding screw-based locomotion: comprehensive experimental results and performance analysis across different media. We designed a mobile test bed for indoor and outdoor experimentation to collect this data. Beyond quantitatively showing the multidomain mobility of screw-based locomotion, we envision future researchers and engineers using the presented results to design effective screw-based locomotion systems.
Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the enviro...
详细信息
ISBN:
(纸本)9798350323658
Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the environment. A challenge, however, is that no model is perfect;moreover, we often do not know where discrepancies between the model's prediction and the actual outcome occur prior to observing executions in the real-world. One way to address this is to bias the planner away from these discrepancies by inflating the cost of states and actions where we previously observed the model to be inaccurate. Making such decisions about where and how to bias purely at the planning-level, however, neglects valuable information from the control-level, which gives a more fine-grained understanding of where and how the model went wrong during execution. Based on this observation, our key idea is to first infer a statistical model over discrepancies in the control-level's model. Then, we translate this model to the planning-level, where we use it to more informatively bias the planner away from states and actions where the model's predicted outcome is likely to be inaccurate. We demonstrate that our framework enables a robot to complete tasks, despite an inaccurate planning model, with greater efficiency than existing approaches. We do so through an experimental evaluation in simulation and real-robot experiments on NASA's Astrobee free-flyer.
Scene graph generation becomes significantly important as it bridges the gap between linguistic and visual information of scenes, facilitating a high-dimensional understanding of scenes. In this paper, we analyze the ...
详细信息
ISBN:
(纸本)9798331517939;9788993215380
Scene graph generation becomes significantly important as it bridges the gap between linguistic and visual information of scenes, facilitating a high-dimensional understanding of scenes. In this paper, we analyze the limitations of current scene graph generation methods induced by the inherent semantic relationship biases embedded in existing datasets. Furthermore, we propose a method to enhance scene graph by leveraging visual language models (VLMs). This approach leverages the strengths of VLMs in understanding and generating triplets with semantic predicates, ensuring unbiased and fine-grained scene graphs.
We present a new data-driven technique for predicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics ...
详细信息
ISBN:
(纸本)9781728196817
We present a new data-driven technique for predicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics engine for analytically computing the gradient of the deviation between predicted motion trajectories and real-world trajectories. This allows to automatically learn and fine-tune the unknown friction coefficients on-the-fly, by minimizing a carefully designed loss function using gradient descent. Experiments show that the predicted trajectories are in excellent agreement with their real-world counterparts. Our proposed approach is computationally superior to existing black-box optimization methods, requiring very few real-world samples for accurate trajectory prediction compared to physics-agnostic techniques, such as neural networks. Experiments also demonstrate that the proposed method allows the robot to quickly adapt to changes in the terrain. Our proposed approach combines the data-efficiency of classical analytical models that are derived from first principles, with the flexibility of data-driven methods, which makes it appropriate for low-cost mobile robots. Project website: https://***/mqxn2x6h
Rapid evolution is underway in the fields of nature language processing and robotics. Our research endeavors to integrate Large Language models (LLMs) with imitation learning, aiming to enhance the efficiency of actio...
详细信息
Research in Deep Neural Networks (DNNs) has gained significant attention from industries and academia achieving unprecedented success. However, DNNs need large-sized datasets and high computation times. In the field o...
详细信息
暂无评论