118 accepted papers
Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks. To address the challenges of effectively combining these modalities, we propose DECO, a decoupled multimodal diffusion transformer that disentangles vision, proprioception, and tactile signal…
Recent text-to-4D generation methods have achieved remarkable progress thanks to advances in text-to-video models. Existing approaches typically reconstruct 4D scenes from generated videos or distill them from pre-trained text-to-video models. However, these methods often restrict the scene to a loc…
General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the training distribution. Therefore, we introduce FunCanon, a framework that converts long-horizon manipulation tasks into sequences of action chunks, each defined by a…
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle to accommodate new task requirements at inference time. Addressing this typically requires costly data recollection and p…
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. Existing single-view approaches often fail in unstructured environments due to limited fields of view, exploration, and g…
Tracking the 6DoF pose of previously unseen objects from monocular RGB videos is crucial for robotic manipulation, yet remains challenging due to depth ambiguity and limited object-centric visual context. Existing trackers often rely on accurate depth sensors, which constrains deployment in low-cost…
We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially-grounded driving actions by leveraging multimodal inputs, including both 2D and 3D instance-aware visual representa
Robots performing collaborative, long-horizon dexterity cell micromanipulation tasks are challenging and practically significant, such as stripping intact cell membranes, which is considered as one of the most technically demanding procedures. Imitation learning approach is expected to address the c
Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on visual observations and robot proprioceptive information often fails to reveal the underlying event transitions. This ra…
Modern manufacturing demands robotic assembly systems with enhanced flexibility and reliability. However, traditional approaches often rely on programming tailored to each product by experts for fixed settings, which are inherently inflexible to product changes and lack the robustness to handle vari…
Optimal path planning aims to determine a sequence of states from a start to a goal while accounting for planning objectives. Popular methods often integrate fixed batch sizes and neglect information on obstacles, which is not problem-specific. This study introduces Adaptively Prolated Trees (APT*),
In the image acquisition process, various forms of degradation, including noise, blur, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from their degraded versions, numer…
Image restoration aims to recover sharp, high-quality images from degraded, low-quality inputs. Existing methods have progressively advanced from task-specific designs to general architectures, all-in-one frameworks, and composite degradation handling. Despite these advances, computational efficienc…
This paper introduces Context Informed Trees (CIT*), a sampling-based motion planning algorithm that enhances exploration efficiency by biasing sampling based on uncertainty estimation from local samples and connectivity information obtained during the search process. CIT* is based on Flexible Infor
As the field of robotics evolves, robots become increasingly multi-functional and complex. Currently, there is a need for solutions that enhance flexibility and computational power without compromising real-time performance. The emergence of fog computing and cloud-native approaches addresses these
Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately re…
The deep learning models has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping. However, the contact information-guided grasping in cluttered environments remains largely underexplored. To address this gap, we have developed ContactDexNet, a method for generat
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally e
Synthesizing diverse, uncertainty-aware grasps for multi-fingered hands from partial observations remains a critical challenge in robot learning. Prior generative methods struggle to model the intricate grasp distribution of dexterous hands and often fail to reason about shape uncertainty inherent i…
Fisheye cameras, renowned for their panoramic field of view (FOV) of 360°, are crucial for surround-view perception in autonomous driving. However, research on object perception in fisheye images lags behind that of standard images. To address this gap, we propose a feature-aligned fisheye object de
This paper presents a novel monitoring framework that infers the level of collision risk for autonomous vehicles (AV s) based on their object detection performance. The framework takes two sets of predictions from different algorithms and associates their inconsistencies with the collision risk via
3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dyna
The whisker-inspired tactile sensor is advantageous for enhancing robotic perception in proximate range and darkness via non-intrusive contacts. However, localizing contact along the whisker shaft is challenging due to the non-injective mapping between tangential contacts and the resulting bending m
Large Language Models (LLMs) have gained popularity in task planning for long-horizon manipulation tasks. To enhance the validity of LLM-generated plans, visual demonstrations and online videos have been widely employed to guide the planning process. However, for manipulation tasks involving subtle
Learning from demonstrations faces challenges in generalizing beyond the training data and often lacks collision awareness. This paper introduces Lan-o3dp, a language-guided object-centric diffusion policy framework that can adapt to unseen situations such as cluttered scenes, shifting camera views,
Learning dexterous manipulation from few-shot demonstrations is a significant yet challenging problem for advanced, human-like robotic systems. Dense distilled feature fields have addressed this challenge by distilling rich semantic features from 2D visual foundation models into the 3D domain. Howev
Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLO
Robotic motion planning faces formidable challenges in constrained environments, particularly in rapidly searching for feasible solutions and converging towards optimal. This study introduces Multi-Sets Tree (MST*), a sampling-based planner designed to accelerate path searching and solution optimiza
Point cloud completion is crucial for reconstructing accurate shapes in many 3D visual applications. Recent approaches incorporate images into the completion pipeline, introducing geometric clues and global constraints. However, their fusion processes often fail to reconstruct detailed parts and mai
This paper proposes a novel dual-filter architecture utilizing RGB-D camera data and dynamic control barrier functions (D-CBFs) for real-time obstacle avoidance in unstructured environments. The proposed method efficiently handles static, suddenly appearing, and dynamic obstacles, maintaining consis
We present TUMTraf VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA pairs, 2,300 object captioning, and 5,700 object grounding annotations, encompassi…
Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precision assembly tasks remains a significant challenge. This paper presents a novel framework that utilizes diffusion models
Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continuous path. However, this process could fail and restart the asymmetric bidirectional search due to the limitations of laz
Image generation has emerged as a potent strategy to enrich training data for unsupervised domain adaptation (UDA) of semantic segmentation in adverse weathers due to the scarcity of labelled target domain data. Previous UDA works commonly utilize generative adversarial networks (GANs) to translate
Insertion is an essential skill for robots in both modern manufacturing and services robotics. In our previous study, we proposed an insertion skill framework based on forcedomain wiggle motion. The main limitation of this method lies in the robot’s inability to adjust its behavior according to chan…
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performance through policy adjustments may adversely affec…
Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However th…
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences. Recent algorithms such as Hindsight Experience Replay (HER) expedite learning by taking advantage of failed trajectories and…
Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tunable parameters from the start of the training, from which the parameters are randomized simultaneously to train a robus…
Curriculum Reinforcement Learning (CRL) is an approach to facilitate the learning process of agents by structuring tasks in a sequence of increasing complexity. Despite its potential, many existing CRL methods struggle to efficiently guide agents toward desired outcomes, particularly in the absence…
This paper presents Ego-Centric Intersection-over-Union (EC-IoU), addressing the limitation of the standard IoU measure in characterizing safety-related performance for object detectors in navigating contexts. Concretely, we propose a weighting mechanism to refine IoU, allowing it to assign a higher…
Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction Informed Trees (FDIT*), a sampling-based planner designed to enhance speed and cost-effectiveness in pathfinding. FDIT*…
Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. However, safe RL often suffers from sample inefficiency, requiring extensive interactions with the environment to learn a safe…
In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent…
Image restoration involves recovering a high-quality image from its corrupted counterpart. This paper presents an effective and efficient framework for image restoration, termed CSNet, based on ``channel + spatial" hybrid frequency modulation. Different feature channels include different degradation…
Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF-UniDA) has emerged to achieve UniDA without access to source data which tends to be more practical due to data protection policies.…
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impr
Fisheye cameras, offering a wide field of view (FOV) of 360◦, are extensively employed for surround-view perception in autonomous driving. Compared with the object detection on the standard images, it lacks studies for fisheye images. Moreover, efficient perception is crucial for autonomous vehicles…
Deep learning has achieved remarkable progress in various applications heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains i.e. making models excl…
Image restoration aims to reconstruct a high-quality image from a degraded low-quality observation. Recently, Transformer models have achieved promising performance on image restoration tasks due to their powerful ability to model long-range dependencies. However, the quadratically growing complexit…
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles. This framework addresses two significant challenges associated with traditional dynamic control ba…
The rising demand for customized products necessitates the integration of multiple robotic systems, underscoring the need for advanced production planning and scheduling. This paper introduces an ontology-based, artificial intelligence-enhanced method for dynamic task planning and scheduling, aimed…
Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of d…
Event cameras can record scene dynamics with high temporal resolution, providing rich scene details for monocular depth estimation (MDE) even at low-level illumination. Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event d…
This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a…
Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust contacts with these fixtures, accurate estimation of the contact state is essential for preventing and rectifying poten…
Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic pr…
Studying the manipulation of deformable linear objects has significant practical applications in industry, including car manufacturing, textile production, and electronics automation. However, deformable linear object manipulation poses a significant challenge in developing planning and control algo…
Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces several challenges in each stage: for warm-up, the widely adopted adversarial training often results in limited performanc…
As a long-standing and challenging task, image deblurring aims to reconstruct the latent sharp image from its degraded counterpart. In this study, to bridge the gaps between degraded/sharp image pairs in the spatial and frequency domains simultaneously, we develop the dual-domain attention mechanis…
Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields. Recently, Transformer models have achieved promising performance on various image restoration tasks. However, their quadratic complexity remains an intractable issue for p…
We present IRNeXt, a simple yet effective convolutional network architecture for image restoration. Recently, Transformer models have dominated the field of image restoration due to the powerful ability of modeling long-range pixels interactions. In this paper, we excavate the potential of the convo…
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-task scenarios due to the insufficient task information provided only by rewards. Language-conditioned meta-RL improves…
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-…
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning,…
Image restoration aims to recover a clean image from various degradations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> haze, snow, and blur, playing an important role in robot vision, autonomous vehicles, and medical imaging. Recently,
This work presents a novel method for point registration in 3D space. The proposed algorithm utilizes transformation-invariant geometry information to estimate the pose of objects based on correspondences between points in two sets. Conventional methods use geometry descriptors to find these corresp…
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image p…
The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause sig…
As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing long-range dependencies, Transformer based methods have achieved promising performance on multifarious image re…
Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation. Nevertheless, most existing learning-based approaches for event optical flow estimation directly remould the pa…
Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes three key processes: communication for sharing, collaboration for integration, and reconstruction for different downstream tasks. Existing methods pursue designing the collaboration process alone, ignoring…
"State-of-the-art lidar-based 3D object detection methods rely on supervised learning and large labeled datasets. However, annotating lidar data is resource-consuming, and depending only on supervised learning limits the applicability of trained models. Self-supervised training strategies can allevi…
Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to…
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different random…
In nature, the movement of quadrupeds is completed under the combined action of the spine and the legs. Inspired by this, this paper explores the effect of a lateral flexing spine on the locomotion of a rat robot. Benefiting from the regular lateral flexion of a soft actuated spine, the rat robot ex…
Grasping unknown objects with multi-fingered hands at high success rates and in real-time is an unsolved problem. Existing methods are limited in the speed of grasp synthesis or the ability to synthesize a variety of grasps from the same observation. We introduce Five-finger Hand Net (FFHNet), an ML…
Physical simulation is an indispensable component of robotics simulation platforms that serves as the basis for a plethora of research directions. Looking strictly at robotics, the common characteristic of the most popular physics engines, such as ODE, DART, MuJoCo, bullet, SimBody, PhysX or RaiSim,…
Stereo matching is an indispensable function that enables machine vision system to obtain depth information of its environment. However, most of existing algorithms rely on conventional camera, which follows the frame-based scheme and has several shortcomings: low dynamic range, low temporal resolut…
Rotation invariance is a crucial property for 3D object classification, which is still a challenging task. State-of-the-art deep learning-based works require a massive amount of data augmentation to tackle this problem. This is however inefficient and classification accuracy suffers a sharp drop in…
The future motion of traffic participants is inherently uncertain. To plan safely, therefore, an autonomous agent must take into account multiple possible trajectory outcomes and prioritize them. Recently, this problem has been addressed with generative neural networks. However, most generative mode…
Inferring a complete 3D geometry given an in-complete point cloud is essential in many vision and robotics applications. Previous work mainly relies on a global feature extracted by a Multi-layer Perceptron (MLP) for predicting the shape geometry. This suffers from a loss of structural details, as i…
LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors,…
This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid mo…
We propose a surface-to-surface (S2S) point registration algorithm by exploiting the Gaussian Process Implicit Surfaces for partially overlapping 3D surfaces to estimate the 6D pose transformation. Unlike traditional approaches, that separate the corresponding search and update steps in the inner lo…
Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides…
An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with t…
For the time varying optimization problem, the tracking error cannot converge to zero at the finite time because of the optimal solution changing over time. This paper proposes a novel varying parameter recurrent neural network (VPRNN) based hierarchical optimization of a 7-DoF surgical manipulator…
In this paper, an improved motion planning scheme is proposed for surgical robot control with multiple active constraints, including joint constraints, joint velocity constraints and remote center of motion constraints. It introduces an improved recurrent neural network (RNN) to optimize the online…
In this paper, an Internet of Things-based human-robot collaborative control scheme is developed in Robot-assisted Minimally Invasive Surgery scenario. A hierarchical operational space formulation is designed to exploit the redundancies of the 7-DoFs redundant manipulator to handle multiple operatio…
This work proposes a probabilistic low level automotive sensor fusion approach using LiDAR, RADAR and camera data. The method is stateless and directly operates on associated data from all sensor modalities. Tracking is not used, in order to reduce the object detection latency and create existence h…
Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes. T…
The complexity of surgical operation can be released significantly if surgical robots can learn the manipulation skills by imitation from complex tasks demonstrations such as puncture, suturing, and knotting, etc.. This paper proposes a reinforcement learning algorithm based manipulation skill trans…
The snake-like robot without wheels is a bio-inspired robot whose high degree of freedom results in a challenge in autonomous locomotion control. The use of a Spiking Neural Network (SNN) which is a biologically plausible artificial neural network can help to achieve the autonomous locomotion behavi…
This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach.…
We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class o…
Pedestrian detection has attracted enormous research attention in the field of Intelligent Transportation System (ITS) due to that pedestrians are the most vulnerable traffic participants. So far, almost all pedestrian detection solutions are based on the conventional frame-based camera. However, th…
Learning by imitation is a powerful way that can reduce the complexly in searching space. It could help the mobile robot to acquire new skills from interaction with a human-being in natural way. In this paper, the dynamic movement primitives (DMPs) is utilized to imitate the trajectory from human wa…
Subretinal injection is known to be a complicated task for ophthalmologists to perform, the main sources of difficulties are the fine anatomy of the retina, insufficient visual feedback, and high surgical precision. Image guided robot-assisted surgery is one of the promising solutions that bring sig…
In this paper, we enhance our knowledge-based and constraint-based approach of robot programming with the concept of Semantic Mates. They describe intended mechanical connections between parts of an assembly. This allows deriving appropriate assembly poses from the type of connection and the geometr…
This paper presents an approach to generate the time-optimal trajectory for a robot manipulator under certain kinematic constraints such as joint position, velocity, acceleration, and jerk limits. This problem of generating a trajectory that takes the minimum time to pass through specified waypoints…
Learning-based methods have demonstrated clear advantages in controlling robot tasks, such as the information fusion abilities, strong robustness, and high accuracy. Meanwhile, the on-board systems of robots have limited computation and energy resources, which are contradictory with state-of-the-art…
Subretinal injection is a delicate and complex microsurgery, which requires surgeons to inject the therapeutic substance in a pre-operatively defined and intra-operatively updated subretinal target area. Due to the lack of subretinal visual feedback, it is hard to sense the insertion depth during th…
In this paper, a lightweight central pattern generator(CPG) model is designed for a snake-like robot, to achieve smooth transition of body shape and locomotion speed. First, based on the convergence behavior of the gradient system, a lightweight CPG model with fast computing time is designed and com…
In this paper, a biologically inspired 3D slithering gait for a snake-like robot is designed and implemented for the purpose of target tracking. First, by balancing the forward speed and the stability of the robot, a straight slithering gait is modelled, under which the robot can march straight, fas…
With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, ob…
In this paper, we introduce a novel robot programming paradigm. It focuses on reducing the required expertise in robotics to a level that allows shop floor workers to use robots in their application domain without the need of extensive training. Our approach is user-centric and can interpret undersp…
In this paper, we propose a framework for prioritized constraint-based specification of robot tasks. This framework is integrated with a cognitive robotic system based on semantic models of processes, objects, and workcells. The target is to enable intuitive (re-)programming of robot tasks, in a way…
The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-dri…
In this paper, we introduce an approach for leveraging CAD description to a semantic level, in order to link additional knowledge to CAD models and to exploit resulting synergy effects.
Intuitive programming of industrial robots is especially important for small and medium-sized enterprises. We evaluated four different input modalities (touch, gesture, speech, 3D tracking device) regarding their preference, usability, and intuitiveness for robot programming.
In this paper, we propose a framework for intuitive task-based programming of robots using geometric inter-relational constraints. The intended applications of this framework are robot programming interfaces that use semantically rich task descriptions, allow intuitive (re-)programming, and are suit…
For robots to solve hard tasks in real-world manufacturing and service contexts, they need to reason about both symbolic and geometric preconditions, and the effects of complex actions. We use an existing Knowledge of Volumes approach to robot task planning (KVP), which facilitates hybrid planning w…
The stereo correspondence problem is still a highly active topic of research with many applications in the robotic domain. Still many state of the art algorithms proposed to date are unable to reasonably handle high resolution images due to their run time complexities or memory requirements. In this…
In order to fully exploit the capabilities of a robotic systems, it is necessary to consider the limitations and errors of actuators and sensors already during the motion planning phase. In this paper, a framework for path planning is introduced, that uses heuristic search to build up a search graph…
The Space Exploration Guided Heuristic Search (SEHS) method solves the motion planning problem, especially for car-like robots, in two steps: a circle-based space exploration in the workspace followed by a circle-guided heuristic search in the configuration space. This paper extends this approach fo…
For intelligent robots to solve real-world tasks, they need to manipulate multiple objects, and perform diverse manipulation actions apart from rigid transfers, such as pushing and sliding. Planning these tasks requires discrete changes between actions, and continuous, collision-free paths that fulf…
In this work we propose a novel approach to the problem of multi-view stereo reconstruction. Building upon the previously proposed PatchMatch stereo and PM-Huber algorithm we introduce an extension to the multi-view scenario that employs an iterative refinement scheme. Our proposed approach uses an…