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IEEE International Conference on robotics and Automation (ICRA)

作者： João Silvério Yanlong Huang German Aerospace Center (DLR) Robotics and Mechatronics Center (RMC) Weβling Germany School of Computing University of Leeds Leeds UK

Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to their encoding of variations, correlations and adaptability to new task conditions. Modulating such primitives, however, is often cumbersome due to the need for parameter re-optimization which frequently entails computationally costly operations. In this paper we derive a non-parametric movement primitive formulation that contains a null space projector. We show that such formulation allows for fast and efficient motion generation and adaptation with computational complexity O(n 2 ) without involving matrix inversions, whose complexity is O(n 3 ). This is achieved by using the null space to track secondary targets, with a precision determined by the training dataset. Using a 2D example associated with time input we show that our non-parametric solution compares favourably with a state-of-the-art parametric approach. For demonstrated skills with high-dimensional inputs we show that it permits on-the-fly adaptation as well.

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Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models

Out of Sight, Still in Mind: Reasoning and Planning about Un...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yixuan Huang Jialin Yuan Chanho Kim Pupul Pradhan Bryan Chen Li Fuxin Tucker Hermans Robotics Center and Kahlert School of Computing University of Utah Salt Lake City UT USA Oregon State University NVIDIA Seattle WA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulation reasoning and planning framework. We propose DOOM and LOOM, which leverage transformer relational dynamics to encode the history of trajectories given partial-view point clouds and an object discovery and tracking engine. Our approaches can perform multiple challenging tasks including reasoning with occluded objects, novel objects appearance, and object reappearance. Throughout our extensive simulation and real-world experiments, we find that our approaches perform well in terms of different numbers of objects and different numbers of distractor actions. Furthermore, we show our approaches outperform an implicit memory baseline.

关键词： Point cloud compression Video tracking Object oriented modeling Transformers Cognition Planning Trajectory

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Exploring Modal Switch in Metamaterial-Based Robots

Exploring Modal Switch in Metamaterial-Based Robots

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

作者： Britton Jordan Daniel S. Esser Jeonghyeon Kim Brian Y. Cho Robert J. Webster Alan Kuntz Robotics Center and the Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Mechanical Engineering Vanderbilt University Nashville TN

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Mechanical metamaterials are microscale patterned structures that are designed to have specific mechanical properties at a macro-scale that are atypical of natural materials. Robotic manipulators composed of these materials can exhibit deformation and motion capabilities that can be customized and easily fabricated. However, as of now, the motion capability of such manipulators are encoded in their physical composition and cannot be changed. This paper presents multimodal metamaterial-based robot prototypes which can switch between the behaviors found in two different metamaterials. Two such robots are explored, a bending/shearing robot and a bending/twisting robot. The robot design is described in detail, including how the robots toggle between behavior modes via mechanical actuation of a sliding rod insert. Multi-modal robots are compared to their single-mode equivalents to characterize their capabilities. The single-mode behaviors are largely preserved in the multi-modal innovations. The multi-modal prototypes also demonstrate variable rigidity. We discuss the feasibility of using robots of this design as part of a robotic surgical system.

关键词： Technological innovation Deformation Prototypes Switches Microsurgery Manipulators Metamaterials Rigidity Polymers Robots

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Combining Thermoelectrics and Low Melting Point Alloys to Create Reconfigurable Stiff-Compliant Manipulators

Combining Thermoelectrics and Low Melting Point Alloys to Cr...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Emily M. McCabe Daniel S. Esser Tayfun Efe Ertop Alan Kuntz Robert J. Webster III Department of Mechanical Engineering Vanderbilt University Nashville TN Robotics Center The Kahlert School of Computing at the University of Utah Salt Lake City UT USA

Soft robots have garnered great interest in recent years due to their ability to navigate complex environments and enhance safety during unplanned collisions. However, their softness typically limits the forces they can apply and payloads they can carry, compared to traditional rigid-link robots. In this paper we seek to create a hybrid manipulator that can switch between a state in which it acts as a soft robot, and a state in which it has a series of selectively stiffenable links. The latter state, accomplished by solidifying chambers of low melting point metal alloy within the robot, is in some ways analogous to a traditional rigid-link manipulator. It also has the added benefit that each “link” can be set to a desired straight or curved shape before solidification and re-shaped when desired. Thermoelectric heat pumps enable local heating and cooling of the alloy, and tendons running along the robot enable actuation. Using a simple two-link prototype, we illustrate how alloy melting and solidification can be used to modify the robot's workspace and payload capacity.

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Directional Stiffness-Switching Soft Robots via Phase-Changing Metallic Spines

Directional Stiffness-Switching Soft Robots via Phase-Changi...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Daniel S. Esser Emily McCabe Tayfun Efe Ertop Alan Kuntz Robert J. Webster Department of Mechanical Engineering Vanderbilt University Nashville TN Robotics Center Kahlert School of Computing at the University of Utah Salt Lake City UT USA

Conventional soft robots are designed with constant, passive stiffness properties, based on desired motion capabilities. The ability to encode two fundamentally different stiffness characteristics promises to enable a single robot to be optimized for multiple divergent tasks simultaneously and this has been previously proposed with a variety of approaches including jamming-based designs. In this paper, we propose phase-changing metallic spines of various geometries to independently control specific directional stiffness parameters of soft robots, changing how they respond to their actuation inputs and external loads. We fabricate spine-like structures using a low melting point alloy (LMPA), enabling us to switch on and off the effects of the stiff metal structure of the overall robot's stiffness during use. Changing soft robot morphology in this manner will enable these robots to adapt to environments and tasks that require divergent motion and force/moment application capabilities.

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Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification

arXiv

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arXiv 2025年

作者： Thompson, Jordan Koe, Ronald Le, Anthony Goodman, Gabriella Brown, Daniel S. Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Biomedical Engineering University of Utah United States

Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in tissue geometries and stiffnesses, these methods do not always perform optimally, especially in out-of-distribution settings. We propose, develop, and test the first application of uncertainty quantification to learned surgical soft-tissue manipulation policies as an early identification system for task failures. We analyze two different methods of uncertainty quantification, deep ensembles and Monte Carlo dropout, and find that deep ensembles provide a stronger signal of future task success or failure. We validate our approach using the physical daVinci Research Kit (dVRK) surgical robot to perform physical soft-tissue manipulation. We show that we are able to successfully detect task failure and request human intervention when necessary while still enabling autonomous manipulation when possible. Our learned tissue manipulation policy with uncertainty-based early failure detection achieves a zero-shot sim2real performance improvement of 47.5% over the prior state of the art in learned soft-tissue manipulation. We also show that our method generalizes well to new types of tissue as well as to a bimanual soft-tissue manipulation task. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic surgery

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DLPO : Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective

arXiv

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arXiv 2025年

作者： Peng, Dengyun Zhou, Yuhang Chen, Qiguang Liu, Jinhao Chen, Jingjing Qin, Libo Research Center for Social Computing and Interactive Robotics Harbin Institute of Technology China School of Computer Science and Engineering Central South University China Fudan University China

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization. Our code is available at https://***/sfasfaffa/DLPO. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Pick and Place Planning is Better than Pick Planning then Place Planning

arXiv

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arXiv 2024年

作者： Shanthi, Mohanraj Devendran Hermans, Tucker The Kahlert School of Computing The Robotics Center University of Utah Salt Lake CityUT84112 United States NVIDIA SeattleWA95050 United States

Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and desired placement locations to ensure success. While several works have examined this joint pick-and-place problem, none have fully leveraged recent learning-based approaches for multifingered grasp planning. We present a modular algorithm for joint pick and place planning that can make use of state of the art grasp classifiers for planning multi-fingered grasps for novel objects from partial view point clouds. We demonstrate our joint pick and place formulation with several costs associated with different placement tasks. Experiments on pick and place tasks with cluttered scenes using a physical robot show that our joint inference method is more successful than a sequential pick then place approach, while also achieving better placement configurations. Copyright © 2024, The Authors. All rights reserved.

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Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong Pan Junran Peng Zhaoxiang Zhang the Center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China IEEE the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth *** methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB ***-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion ***,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with *** from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth *** we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each ***,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input *** method is the first Swin-Transformer-based backbone architecture for monocular 3D object *** experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer

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V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

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

作者： Herbert Wright Weiming Zhi Matthew Johnson-Roberson Tucker Hermans University of Utah Robotics Center and Kahlert School of Computing University of Utah Salt Lake City UT USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA NVIDIA Corporation Santa Clara CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGBD image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this problem do not capture accurate uncertainty, which is required to subsequently produce safe motion plans. In this paper, we cast the representation of 3D tabletop scenes as a multi-class classification problem. To tackle this, we introduce V-PRISM, a framework and method for robustly creating probabilistic 3D segmentation maps of tabletop scenes. Our maps contain both occupancy estimates, segmentation information, and principled uncertainty measures. We evaluate the robustness of our method in (1) procedurally generated scenes using open-source object datasets, and (2) real-world tabletop data collected from a depth camera. Our experiments show that our approach outperforms alternative continuous reconstruction approaches that do not explicitly reason about objects in a multi-class formulation.

关键词： Image segmentation Three-dimensional displays Uncertainty Motion segmentation Robot vision systems Measurement uncertainty Probabilistic logic Robustness Intelligent robots Image reconstruction

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