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V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

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V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

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

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

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

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

Exploring Modal Switch in Metamaterial-Based Robots

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Exploring Modal Switch in Metamaterial-Based Robots

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

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

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

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

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

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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Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors

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arXiv 2024年

作者： Wright, Herbert Zhi, Weiming Johnson-Roberson, Matthew Hermans, Tucker University of Utah Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT United States Robotics Institute Carnegie Mellon University PittsburghPA United States NVIDIA Corporation SeattleWA United States

Constructing 3D representations of object geometry is critical for many downstream robotics tasks, particularly tabletop manipulation problems. These representations must be built from potentially noisy partial observations. In this work, we focus on the problem of reconstructing a multi-object scene from a single RGBD image, generally from a fixed camera in the scene. Traditional scene representation methods generally cannot infer the geometry of unobserved regions of the objects from the image. Attempts have been made to leverage deep learning to train on a dataset of observed objects and representations, and then generalize to new observations. However, this can be brittle to noisy real-world observations and objects not contained in the dataset, and cannot reason about their confidence. We propose BRRP, a reconstruction method that leverages preexisting mesh datasets to build an informative prior during robust probabilistic reconstruction. In order to make our method more efficient, we introduce the concept of retrieval-augmented prior, where we retrieve relevant components of our prior distribution during inference. The prior is used to estimate the geometry of occluded portions of the in-scene objects. Our method produces a distribution over object shape that can be used for reconstruction or measuring uncertainty. We evaluate our method in both simulated scenes and in the real world. We demonstrate the robustness of our method against deep learning-only approaches while being more accurate than a method without an informative prior. © 2024, CC BY.

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

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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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V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

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arXiv 2024年

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 RGB-D 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. © 2024, CC BY.

关键词： Uncertainty analysis

Toward a Millimeter-Scale Tendon-Driven Continuum Wrist with Integrated Gripper for Microsurgical Applications

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arXiv 2023年

作者： Leavitt, Alexandra Lam, Ryan Taylor, Nichols Crawford Drew, Daniel S. Kuntz, Alan Robotics Center United States Kahlert School of Computing United States Department of Electrical and Computer Engineering University of Utah United States

Microsurgery is a particularly impactful yet challenging form of *** assisted microsurgery has the potential to improve surgical dexterity andenable precise operation on such small scales in ways not previously *** microsurgery is a particularly challenging domain in part due tothe lack of dexterity that is achievable with rigid instruments insertedthrough the eye. In this work, we present a new design for a millimeter-scale,dexterous wrist intended for microsurgery applications. The wrist is createdvia a state-of-the-art two-photon-polymerization (2PP) microfabricationtechnique, enabling the wrist to be constructed of flexible material withcomplex internal geometries and critical features at the micron-scale. Thewrist features a square cross section with side length of 1.25 mm and totallength of 3.75 mm. The wrist has three tendons routed down its length which,when actuated by small-scale linear actuators, enable bending in any plane. Wepresent an integrated gripper actuated by a fourth tendon routed down thecenter of the robot. We evaluate the wrist and gripper by characterizing itsbend-angle. We achieve more than 90 degrees bending in both axes. Wedemonstrate out of plane bending as well as the robot\'s ability to grip whileactuated. Our integrated gripper/tendon-driven continuum robot design andmeso-scale assembly techniques have the potential to enable small-scale wristswith more dexterity than has been previously demonstrated. Such a wrist couldimprove surgeon capabilities during teleoperation with the potential to improvepatient outcomes in a variety of surgical applications, including intraocularsurgery. Copyright © 2023, The Authors. All rights reserved.

关键词： Grippers