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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24815 条记录，以下是81-90 订阅

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GG-LLM: Geometrically Grounding Large Language models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning

GG-LLM: Geometrically Grounding Large Language Models for Ze...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Graule, Moritz A. Isler, Volkan Samsung AI Ctr New York New York NY 10010 USA

ISBN: (纸本)9798350384581;9798350384574

A robot in a human-centric environment needs to account for the human's intent and future motion in its task and motion planning to ensure safe and effective operation. This requires symbolic reasoning about probable future actions and the ability to tie these actions to specific locations in the physical environment. While one can train behavioral models capable of predicting human motion from past activities, this approach requires large amounts of data to achieve acceptable long-horizon predictions. More importantly, the resulting models are constrained to specific data formats and modalities. Moreover, connecting predictions from such models to the environment at hand to ensure the applicability of these predictions is an unsolved problem. We present a system that utilizes a Large Language Model (LLM) to infer a human's next actions from a range of modalities without fine-tuning. A novel aspect of our system that is critical to robotics applications is that it links the predicted actions to specific locations in a semantic map of the environment. Our method leverages the fact that LLMs, trained on a vast corpus of text describing typical human behaviors, encode substantial world knowledge, including probable sequences of human actions and activities. We demonstrate how these localized activity predictions can be incorporated in a human-aware task planner for an assistive robot to reduce the occurrences of undesirable human-robot interactions by 29.2% on average.

关键词： Semantics

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A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction

A Novel Benchmarking Paradigm and a Scale- and Motion-Aware ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Rasouli, Amir Huawei Technol Canada Markham ON Canada

ISBN: (纸本)9798350384581;9798350384574

In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction models. In this regard, we also propose a new metric for more effective ranking within the scenario-based evaluation. We conduct extensive empirical studies of existing models on these scenarios to expose shortcomings and strengths of different approaches. The scenario-based analysis highlights the importance of using multimodal sources of information and challenges caused by inadequate modeling of ego-motion and scale of pedestrians. To this end, we propose a novel egocentric trajectory prediction model that benefits from multimodal sources of data fused in an effective and efficient step-wise hierarchical fashion and two auxiliary tasks designed to learn more robust representation of scene dynamics. We conduct empirical evaluation on common benchmark datasets and show that our model not only achieves state-of-the-art performance, but also significantly improves performance by up to 39% in challenging scenarios, such as high ego-speed, compared to the past arts(1).

关键词： Prediction models

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Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation models

Drive Anywhere: Generalizable End-to-end Autonomous Driving ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wang, Tsun-Hsuan Maalouf, Alaa Xia, Wei Bao, Yutong Amini, Alexander Rosman, Guy Karaman, Sertac Rus, Daniela MIT CSAII Cambridge MA 02139 USA Shanghai Jiao Tong Univ Shanghai Peoples R China TRI Los Altos CA USA MIT LIDS Cambridge MA USA

ISBN: (纸本)9798350384581;9798350384574

As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning. However, existing systems grapple with challenges such as unexpected open set environments and the complexity of black-box models. At the same time, the evolution of deep learning introduces larger, multimodal foundational models, offering multi-modal visual and textual understanding. In this paper, we harness these multimodal foundation models to enhance the robustness and adaptability of autonomous driving systems. We introduce a method to extract nuanced spatial features from transformers and the incorporation of latent space simulation for improved training and policy debugging. We use pixel/patch-aligned feature descriptors to expand foundational model capabilities to create an end-to-end multimodal driving model, demonstrating unparalleled results in diverse tests. Our solution combines language with visual perception and achieves significantly greater robustness on out-of-distribution situations.

关键词： Robustness (control systems)

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Grounding Conversational Robots on Vision Through Dense Captioning and Large Language models

Grounding Conversational Robots on Vision Through Dense Capt...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Grassi, Lucrezia Hong, Zhouyang Recchiuto, Carmine Tommaso Sgorbissa, Antonio Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 13 I-16145 Genoa Italy

ISBN: (纸本)9798350384581;9798350384574

This work explores a novel approach to empowering robots with visual perception capabilities using textual descriptions. Our approach involves the integration of GPT-4 with dense captioning, enabling robots to perceive and interpret the visual world through detailed text-based descriptions. To assess both user experience and the technical feasibility of this approach, experiments were conducted with human participants interacting with a Pepper robot equipped with visual capabilities. The results affirm the viability of the proposed approach, allowing to perform vision-based conversations effectively, despite processing time limitations.

关键词： Robot vision

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CausalAgents: A Robustness Benchmark for Motion Forecasting

CausalAgents: A Robustness Benchmark for Motion Forecasting

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sun, Liting Roelofs, Rebecca Caine, Ben Refaat, Khaled S. Sapp, Ben Ettinger, Scott Chai, Wei Waymo Mountain View CA 94043 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350384581;9798350384574

As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable. In this paper, we examine the robustness of motion forecasting to non-causal perturbations. We construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. Specifically, we conduct an extensive labeling effort to identify causal agents, or agents whose presence influences human drivers' behavior, in the Waymo Open Motion Dataset (WOMD), and we use these labels to perturb the data by deleting non-causal agents from the scene. We evaluate a diverse set of state-of-the-art deep-learning models on our proposed benchmark and find that all evaluated models exhibit large shifts under non-causal perturbation: we observe a surprising 25-38% relative change in minADE as compared to the original. In addition, we investigate techniques to improve model robustness, including increasing the training dataset size and using targeted data augmentations that randomly drop non-causal agents throughout training. Finally, we release the causal agent labels as an extension to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting.

关键词： Prediction models

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Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation models and Self-Recovery

Self-Recovery Prompting: Promptable General Purpose Service ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Shirasaka, Mimo Matsushima, Tatsuya Tsunasmia, Soshi Ikeda, Yuya Horo, Aoi Ikoma, So Tsuji, Chikaha Wada, Hikaru Omija, Tsunekazu Komukai, Dai Matsuo, Yutaka Iwasawa, Yusuke Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350384581;9798350384574

A general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environments. In this paper, we first developed a top-level GPSR system for worldwide competition (RoboCup@Home 2023) based on multiple foundation models. This system is both generalizable to variations and adaptive by prompting each model. Then, by analyzing the performance of the developed system, we found three types of failure in more realistic GPSR application settings: insufficient information, incorrect plan generation, and plan execution failure. We then propose the self-recovery prompting pipeline, which explores the necessary information and modifies its prompts to recover from failure. We experimentally confirm that the system with the self-recovery mechanism can accomplish tasks by resolving various failure cases. https://***/view/srgpsr

关键词： Global positioning system

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Spider Arm: Design and fabrication of a cost-effective six-axis robotic arm featuring a novel double-ring load transfer mechanism 7

Spider Arm: Design and fabrication of a cost-effective six-a...

引用

7th international conference on Mechatronics, robotics and automation

作者： Mathavaraj, Narenthiran Kute, Iishan Ramkumar, Hurath Natl Inst Technol Dept Elect & Commun Engn Tiruchirappalli India Natl Inst Technol Dept Mech Engn Tiruchirappalli India

ISBN: (纸本)9798350352481;9798350352474

This paper is part of an ongoing study at the labs of Spider R&D (The official research and development club of the National Institute of Technology, Tiruchirappalli). This paper presents a 6-degree-of-freedom (DOF) robotic arm being built at our labs for research on pick-and-place movements, teleoperation, and integration with large language models (LLMs). The arm being built is 70 cm in length and has a payload capacity of 0.5 kg. This arm features a novel double-ring load transfer mechanism at the base to ensure that none of the arm's load is exerted on the base motor vertically. Other joints of the arm are driven by low to high-torque belt drives. The end goal of this arm is for its integration with LLMs as planners for household tasks.

关键词： 6DOF Robotic Arm Machine Learning Large Language models

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Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language models

Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-La...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kapelyukh, Ivan Ren, Yifei Alzugaray, Ignacio Johns, Edward Imperial Coll London Robot Learning Lab London England Dyson Robot Lab London England

ISBN: (纸本)9798350384581;9798350384574

We introduce Dream2Real, a robotics framework which integrates vision-language models (VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the robot autonomously constructing a 3D representation of the scene, where objects can be rearranged virtually and an image of the resulting arrangement rendered. These renders are evaluated by a VLM, so that the arrangement which best satisfies the user instruction is selected and recreated in the real world with pick-and-place. This enables language-conditioned rearrangement to be performed zero-shot, without needing to collect a training dataset of example arrangements. Results on a series of real-world tasks show that this framework is robust to distractors, controllable by language, capable of understanding complex multi-object relations, and readily applicable to both tabletop and 6-DoF rearrangement tasks. Videos are available on our webpage at: https://***/dream2real.

关键词： Visual languages

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CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction models for Autonomous Driving

CRITERIA: a New Benchmarking Paradigm for Evaluating Traject...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chen, Changhe Pourkeshavarz, Mozhgan Rasouli, Amir Univ Toronto Toronto ON Canada Huawei Markham ON Canada Huawei Technol Canada Noahs Ark Lab Markham ON Canada

ISBN: (纸本)9798350384581;9798350384574

Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scenarios, such as cruising, and distance-based metrics that are computed by averaging over all scenarios. Following such a regiment provides a little insight into the properties of the models both in terms of how well they can handle different scenarios and how admissible and diverse their outputs are. There exist a number of complementary metrics designed to measure the admissibility and diversity of trajectories, however, they suffer from biases, such as length of trajectories. In this paper, we propose a new benChmarking paRadIgm for evaluaTing trajEctoRy predIction Approaches (CRITERIA). Particularly, we propose 1) a method for extracting driving scenarios at varying levels of specificity according to the structure of the roads, models' performance, and data properties for fine-grained ranking of prediction models;2) A set of new bias-free metrics for measuring diversity, by incorporating the characteristics of a given scenario, and admissibility, by considering the structure of roads and kinematic compliancy, motivated by real-world driving constraints;3) Using the proposed benchmark, we conduct extensive experimentation on a representative set of the prediction models using the large scale Argoverse dataset. We show that the proposed benchmark can produce a more accurate ranking of the models and serve as a means of characterizing their behavior. We further present ablation studies to highlight contributions of different elements that are used to compute the proposed metrics(1).

关键词： Trajectories

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Towards Feasible Dynamic Grasping: Leveraging Gaussian Process Distance Field, SE(3) Equivariance, and Riemannian Mixture models

Towards Feasible Dynamic Grasping: Leveraging Gaussian Proce...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Choi, Ho Jin Figueroa, Nadia Univ Penn Dept Mech Engn & Appl Mech Sch Engn & Appl Sci Philadelphia PA 19104 USA

ISBN: (纸本)9798350384581;9798350384574

This paper introduces a novel approach to improve robotic grasping in dynamic environments by integrating Gaussian Process Distance Fields (GPDF), SE(3) equivariant networks, and Riemannian Mixture models. The aim is to enable robots to grasp moving objects effectively. Our approach comprises three main components: object shape reconstruction, grasp sampling, and implicit grasp pose selection. GPDF accurately models the shape of objects, which is essential for precise grasp planning. SE(3) equivariance ensures that the sampled grasp poses are equivariant to the object's pose changes, enhancing robustness in dynamic scenarios. Riemannian Gaussian Mixture models are employed to assess reachability, providing a feasible and adaptable grasping strategies. Feasible grasp poses are targeted by novel task or joint space reactive controllers formulated using Gaussian Mixture models and Gaussian Processes. This method resolves the challenge of discrete grasp pose selection, enabling smoother grasping execution. Experimental validation confirms the effectiveness of our approach in generating feasible grasp poses and achieving successful grasps in dynamic environments. By integrating these advanced techniques, we present a promising solution for enhancing robotic grasping capabilities in real-world scenarios.

关键词： Gaussian distribution

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