Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks

• URL: http://arxiv.org/abs/2103.05140v1
• Date: Mon, 8 Mar 2021 23:46:48 GMT
• Title: Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks
• Authors: Wenzhao Lian, Tim Kelch, Dirk Holz, Adam Norton, and Stefan Schaal
• Abstract summary: It remains frequently unclear what is the baseline state-of-the-art performance and what are the bottleneck problems. We evaluate some off-the-shelf (OTS) industrial solutions on a recently introduced benchmark, the National Institute of Standards and Technology (NIST) Assembly Task Boards.
• Score: 9.125933436783681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback. However, it remains frequently unclear what is the baseline state-of-the-art performance and what are the bottleneck problems. In this work, we evaluate some off-the-shelf (OTS) industrial solutions on a recently introduced benchmark, the National Institute of Standards and Technology (NIST) Assembly Task Boards. A set of assembly tasks are introduced and baseline methods are provided to understand their intrinsic difficulty. Multiple sensor-based robotic solutions are then evaluated, including hybrid force/motion control and 2D/3D pattern matching algorithms. An end-to-end integrated solution that accomplishes the tasks is also provided. The results and findings throughout the study reveal a few noticeable factors that impede the adoptions of the OTS solutions: expertise dependent, limited applicability, lack of interoperability, no scene awareness or error recovery mechanisms, and high cost. This paper also provides a first attempt of an objective benchmark performance on the NIST Assembly Task Boards as a reference comparison for future works on this problem.

Related papers

• Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends [1.124958340749622]
  This paper reviews prominent research in Understanding (CU) It focuses on State-of-The-Art (SoTA) frameworks that employ transformers within End-to-End (E2E) solutions. This article identifies key challenges and outlines promising future directions for advancing CU solutions.
  arXiv  Detail & Related papers  (2024-10-05T16:26:44Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation [76.67608003501479]
  We introduce and specify an evaluation protocol defining a range of domain-related metrics computed on the basics of the primary evaluation indicators. The results of such a comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.
  arXiv  Detail & Related papers  (2024-07-20T16:37:21Z)
• Robustness Assessment of Mathematical Reasoning in the Presence of Missing and Contradictory Conditions [48.251724997889184]
  We develop a benchmark called Problems with Missing and Contradictory conditions (PMC) We introduce two novel metrics to evaluate the performance of few-shot prompting methods in these scenarios. We propose a novel few-shot prompting method called SMT-LIB Prompting (SLP), which utilizes the SMT-LIB language to model the problems instead of solving them directly.
  arXiv  Detail & Related papers  (2024-06-07T16:24:12Z)
• SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving [64.38649623473626]
  Large Language Models (LLMs) have driven substantial progress in artificial intelligence. We propose a novel framework called textbfSEquential subtextbfGoal textbfOptimization (SEGO) to enhance LLMs' ability to solve mathematical problems.
  arXiv  Detail & Related papers  (2023-10-19T17:56:40Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [88.35145788575348]
  Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing. The lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications. We construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets.
  arXiv  Detail & Related papers  (2023-01-31T01:24:45Z)
• Task Allocation using a Team of Robots [29.024300177453824]
  We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can vary depending on the feasibility constraints, objective functions, and the level of dynamically changing information.
  arXiv  Detail & Related papers  (2022-07-20T04:49:11Z)
• Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges [27.474011433615317]
  Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks. We investigate the factors that contribute to the performance differences between task-agnostic CL and multi-task (MTL) agents.
  arXiv  Detail & Related papers  (2022-05-28T17:59:00Z)
• Fast Object Segmentation Learning with Kernel-based Methods for Robotics [21.48920421574167]
  Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation. We propose a novel architecture for object segmentation, that overcomes this problem and provides comparable performance in a fraction of the time required by the state-of-the-art methods. Our approach is validated on the YCB-Video dataset which is widely adopted in the computer vision and robotics community.
  arXiv  Detail & Related papers  (2020-11-25T15:07:39Z)
• Learning 3D Part Assembly from a Single Image [20.175502864488493]
  We introduce a novel problem, single-image-guided 3D part assembly, along with a learningbased solution. We study this problem in the setting of furniture assembly from a given complete set of parts and a single image depicting the entire assembled object.
  arXiv  Detail & Related papers  (2020-03-21T21:19:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.