检索结果-内蒙古大学图书馆

A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding

学校读者我要写书评

暂无评论

A Virtual Reality Framework for Human-Robot Collaboration in...

IEEE-RAS International Conference on Humanoid Robots

作者： Marco Moletta Maciej K. Wozniak Michael C. Welle Danica Kragic Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden

关键词：

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

学校读者我要写书评

暂无评论

Energy-Optimized Planning in Non-Uniform Wind Fields with Fi...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yufei Duan Florian Achermann Jaeyoung Lim Roland Siegwart Robotics Perception and Learning Lab KTH Royal Institude of Technology Stockholm Autonomous Systems Lab ETH Zürich Zürich Switzerland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limits, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the characteristics of the planner with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes.

关键词： Wind Costs Uncertainty Wind energy Wind speed Energy efficiency Planning Safety Trajectory Wind forecasting

Raising Body Ownership in End-to-End Visuomotor Policy learning via Robot-Centric Pooling

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhuang, Zheyu Kyrki, Ville Kragic, Danica The Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden Aalto University Espoo Finland

We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomotor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot's proprioceptive states, thereby extracting robot-centric image representations for policy learning. Leveraging contrastive learning techniques, RcP integrates seamlessly with existing visuomotor policy learning frameworks and is trained jointly with the policy using the same dataset, requiring no extra data collection involving self-distractors. We evaluate the proposed method with reaching tasks in both simulated and real-world settings. The results demonstrate that RcP significantly enhances the policies' robustness against various unseen distractors, including self-distractors, positioned at different locations. Additionally, the inherent robot-centric characteristic of RcP enables the learnt policy to be far more resilient to aggressive pixel shifts compared to the baselines. © 2024, CC BY.

关键词： Contrastive learning

Relative Representations: Topological and Geometric Perspectives

学校读者我要写书评

暂无评论

arXiv 2024年

作者： García-Castellanos, Alejandro Marchetti, Giovanni Luca Kragic, Danica Scolamiero, Martina Amsterdam Machine Learning Lab University of Amsterdam Netherlands Department of Mathematics KTH Royal Institute of Technology Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching. © 2024, CC BY.

关键词： Topology

learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Weiwei, Liu Wenxuan, Hu Wei, Jing Lanxin, Lei Lingping, Gao Yong, Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Advanced Perception on Robotics and Intelligent Learning Lab Huzhou Institute Zhejiang University Huzhou China College of Information Engineering Huzhou University Huzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

Autonomous vehicles trained through Multi-Agent Reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

AdaFold: Adapting Folding Trajectories of Cloths via Feedback-loop Manipulation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Longhini, Alberta Welle, Michael C. Erickson, Zackory Kragic, Danica Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden Carnegie Mellon University Pittsburgh United States

We present AdaFold, a model-based feedback-loop framework for optimizing folding trajectories. AdaFold extracts a particle-based representation of cloth from RGB-D images and feeds back the representation to a model predictive control to re-plan folding trajectory at every time-step. A key component of AdaFold that enables feedback-loop manipulation is the use of semantic descriptors extracted from geometric features. These descriptors enhance the particle representation of the cloth to distinguish between ambiguous point clouds of differently folded cloths. Our experiments demonstrate AdaFold’s ability to adapt folding trajectories of cloths with varying physical properties and generalize from simulated training to real-world execution. Videos can be found on our project website: https://*** Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dunnhofer, Matteo Micheloni, Christian Machine Learning and Perception Lab University of Udine Udine Italy

How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one. The proposed methodology is evaluated extensively and the comparison with several other solutions reveals that it competes favourably with the state-of-the-art on the most popular long-term visual tracking benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Pitz, Johannes Röstel, Lennart Sievers, Leon Burschka, Darius Bäuml, Berthold Learning AI for Dextrous Robots Lab Technical University of Munich Germany Germany Machine Vision and Perception Group Technical University of Munich Germany

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ∼90% even for non-convex shapes. Website: https://***/manipulation/iros24 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Duan, Yufei Achermann, Florian Lim, Jaeyoung Siegwart, Roland Robotics Perception and Learning Lab KTH Royal Institude of Technology Stockholm114 28 Sweden Autonomous Systems Lab ETH Zürich Zürich8092 Switzerland

关键词： Energy efficiency