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RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning

IEEE robotics and Automation Letters 2024年第1期9卷 9-16页

作者： Collins, Jack Robson, Mark Yamada, Jun Sridharan, Mohan Janik, Karol Posner, Ingmar University of Oxford Applied AI Lab Oxford Robotics Institute OxfordOX1 3PJ United Kingdom University of Birmingham BirminghamB152TT United Kingdom School of Computer Science University of Birmingham Intelligent Robotics Lab BirminghamB152TT United Kingdom The Manufacturing Technology Centre CoventryCV7 9JU United Kingdom

We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configurations using pegs as fasteners. As such, it assesses planning and execution capabilities, and poses challenges in perception, reasoning, manipulation, diagnostics, fault recovery, and goal parsing. RAMP has been designed to be accessible and extensible. Parts are either 3D printed or otherwise constructed from materials that are readily obtainable. The design of parts and detailed instructions are publicly available. In order to broaden community engagement, RAMP incorporates fixtures such as April Tags which enable researchers to focus on individual sub-tasks of the assembly challenge if desired. We provide a full digital twin as well as rudimentary baselines to enable rapid progress. Our vision is for RAMP to form the substrate for a community-driven endeavour that evolves as capability matures. © 2016 IEEE.

关键词： Robotic assembly

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Intersection Coordination with Priority-Based Search for Autonomous Vehicles 37

Intersection Coordination with Priority-Based Search for Aut...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Li, Jiaoyang Hoang, The Anh Lin, Eugene Vu, Hai L. Koenig, Sven Robotics Institute Carnegie Mellon University United States Department of Civil Engineering Monash University Australia Computer Science Department University of Southern California United States

ISBN: (纸本)9781577358800

The development of connected and autonomous vehicles opens an opportunity to manage intersections without signals. One promising approach is to use a central autonomous intersection manager to optimize the movement of the vehicles in the intersection. Existing work uses Mixed Integer Linear Programming (MILP) to find optimal solutions for this problem but is time-consuming and cannot be applied in real-time. On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. Theoretically, PSL is complete and polynomial-time in the number of vehicles. Empirically, PSL runs significantly faster with only a slight compromise in the solution quality than the optimal MILP method. It also generates significantly better solutions with a slightly larger runtime than the traditional First-Come-First-Served strategy. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Autonomous vehicles

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DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark

DarkGS: Learning Neural Illumination and 3D Gaussians Religh...

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

作者： Tianyi Zhang Kaining Huang Weiming Zhi Matthew Johnson-Roberson Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Humans have the remarkable ability to construct consistent mental models of an environment, even under limited or varying levels of illumination. We wish to endow robots with this same capability. In this paper, we tackle the challenge of constructing a photorealistic scene representation under poorly illuminated conditions and with a moving light source. We approach the task of modeling illumination as a learning problem, and utilize the developed illumination model to aid in scene reconstruction. We introduce an innovative framework that uses a data-driven approach, Neural Light Simulators (NeLiS), to model and calibrate the camera-light system. Furthermore, we present DarkGS, a method that applies NeLiS to create a relightable 3D Gaussian scene model capable of real-time, photorealistic rendering from novel viewpoints. We show the applicability and robustness of our proposed simulator and system in a variety of real-world environments. Code released at https://***/proj/***

关键词： Solid modeling Three-dimensional displays Codes Lighting Rendering (computer graphics) Robustness Real-time systems Cognitive science Intelligent robots Light sources

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Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine

Adaptive Planning and Control with Time-Varying Tire Models ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Dvij Kalaria Qin Lin John M. Dolan Robotics Institute Carnegie Mellon University Computer Science Department Cleveland State University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning, and control. Adding to this complexity is the need to accurately identify vehicle model parameters governing lateral tire slip effects, which can evolve over time due to factors such as tire wear and tear. Current approaches to this problem typically either propose offline model identification methods or rely on initial parameters within a narrow range (typically within 15-20% of the actual values). However, these approaches fall short in accounting for significant changes in tire models that can occur during actual races, particularly when pushing the vehicle to its handling limits. We present a unified framework that not only learns the tire model in real time from collected data but also adapts the model to environmental changes, even when the model parameters exhibit substantial deviations. The friction estimation, obtained as a byproduct from the learning results, facilitates the selection of the optimal racing line from a library for adaptive speed planning. We validate our approach through testing in simulators, encompassing a 1:43 scale race car and a full-size car, and also through experiments with a physical F1/10 autonomous race car.

关键词： Adaptation models Friction Tires Data models Real-time systems Planning Automobiles

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Closed-loop contrast source regulation through real-time spectroscopic photoacoustic imaging: phantom evaluation

Closed-loop contrast source regulation through real-time spe...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Murakami, Ryo Gao, Shang Zhang, Haichong K. Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Biomedical Engineering Worcester Polytechnic Institute WorcesterMA United States Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781510658639

Photoacoutisc (PA) imaging can not only depict the geometrical features of a target like ultrasound but also allow to decompose of the contribution from different material sources based on their own specific optical absorption characteristics. With the potential of PA imaging, how to utilize the extracted information beyond diagnosis and monitoring is an open research question. Despite this interest in the field, the PA-based automation of regulating conditions of contrast sources such as position and concentration has not been actively studied. This regulation has the potential to be used for several applications including precise/long-term dose control of exogenous contrast agents in the context of drug delivery. In addition, material decomposition allows for the selective regulation of specific contrast agents. This would be advantageous in clinical situations where it is necessary to use multiple types of contrast agents simultaneously and independently. In this report, we propose a system architecture to realize closed-loop source regulation through real-time spectroscopic PA imaging and develop an experimental setup to demonstrate the feasibility of this approach. The system autonomously terminated the ICG flow within the target performance range which is defined considering the specifications of the system. © 2023 SPIE.

关键词： Inverse problems

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Versatile Navigation Under Partial Observability via Value-Guided Diffusion Policy

Versatile Navigation Under Partial Observability via Value-G...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Gengyu Zhang Hao Tang Yan Yan Department of Computer Science Illinois Institute of Technology USA Robotics Institute Carnegie Mellon University USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature, while the latter either assumes full observability or struggles to adapt to unfamiliar scenarios, due to strong couplings with behavior cloning from experts. To address these deficiencies, we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability. Specifically, our value-guided diffusion policy first generates plans to predict actions across various timesteps, providing ample foresight to the planning. It then employs a differentiable planner with state estimations to derive a value function, directing the agent's exploration and goal-seeking behaviors without seeking experts while explicitly addressing partial observability. During inference, our policy is further enhanced by a best-plan-selection strategy, substantially boosting the planning success rate. Moreover, we propose projecting point clouds, derived from RGB-D inputs, onto 2D grid-based bird-eye- view maps via semantic segmentation, generalizing to 3D environments. This simple yet effective adaption enables zero-shot transfer from 2D-trained policy to 3D, cutting across the laborious training for 3D policy, and thus certifying our versatility. Experimental results demonstrate our superior performance, particularly in navigating situations beyond expert demonstrations, surpassing state-of-the-art autoregressive and diffusion-based baselines for both 2D and 3D scenarios.

关键词： Training Point cloud compression Three-dimensional displays Uncertainty Navigation Semantic segmentation Planning

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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Engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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RL-VLM-F: reinforcement learning from vision language foundation model feedback 24

RL-VLM-F: reinforcement learning from vision language founda...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yufei Wang Zhanyi Sun Jesse Zhang Zhou Xian Erdem Biyik David Held Zackory Erickson Robotics Institute Carnegie Mellon University Department of Computer Science University of Southern California

Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a method that automatically generates reward functions for agents to learn new tasks, using only a text description of the task goal and the agent's visual observations, by leveraging feedbacks from vision language foundation models (VLMs). The key to our approach is to query these models to give preferences over pairs of the agent's image observations based on the text description of the task goal, and then learn a reward function from the preference labels, rather than directly prompting these models to output a raw reward score, which can be noisy and inconsistent. We demonstrate that RL-VLM-F successfully produces effective rewards and policies across various domains -- including classic control, as well as manipulation of rigid, articulated, and deformable objects -- without the need for human supervision, outperforming prior methods that use large pretrained models for reward generation under the same assumptions. Videos can be found on our project website: https://***/.

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Estimating Material Properties of Interacting Objects Using Sum-GP-UCB

Estimating Material Properties of Interacting Objects Using ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： M. Yunus Seker Oliver Kroemer The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of observations. Our focus is on estimating these properties based on observations of scenes with different sets of interacting objects. We propose an approach that exploits the structure of the reward function by modeling the reward for each observation separately and using only the parameters of the objects in that scene as inputs. The resulting lower-dimensional models generalize better over the parameter space, which in turn results in a faster optimization. To speed up the optimization process further, and reduce the number of simulation runs needed to find good parameter values, we also propose partial evaluations of the reward function, wherein the selected parameters are only evaluated on a subset of real world evaluations. The approach was successfully evaluated on a set of scenes with a wide range of object interactions, and we showed that our method can effectively perform incremental learning without resetting the rewards of the gathered observations.

关键词： Incremental learning Bayes methods Object recognition Optimization Robots Material properties

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