SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes

• URL: http://arxiv.org/abs/2306.15620v3
• Date: Mon, 11 Mar 2024 06:20:07 GMT
• Title: SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
• Authors: Ninad Khargonkar, Sai Haneesh Allu, Yangxiao Lu, Jishnu Jaykumar P, Balakrishnan Prabhakaran, Yu Xiang
• Abstract summary: We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies.
• Score: 5.80109297939618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods.

Related papers

• Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
  Differentiable simulation has become a powerful tool for system identification. Our approach calibrates object properties by using information from the robot, without relying on data from the object itself. We demonstrate the effectiveness of our method on a low-cost robotic platform.
  arXiv  Detail & Related papers  (2024-10-04T20:48:38Z)
• Tiny Robotics Dataset and Benchmark for Continual Object Detection [6.4036245876073234]
  This work introduces a novel benchmark to evaluate the continual learning capabilities of object detection systems in tiny robotic platforms. Our contributions include: (i) Tiny Robotics Object Detection (TiROD), a comprehensive dataset collected using a small mobile robot, designed to test the adaptability of object detectors across various domains and classes; (ii) an evaluation of state-of-the-art real-time object detectors combined with different continual learning strategies on this dataset; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field.
  arXiv  Detail & Related papers  (2024-09-24T16:21:27Z)
• Generalized Robot Learning Framework [10.03174544844559]
  We present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots.
  arXiv  Detail & Related papers  (2024-09-18T15:34:31Z)
• IRASim: Learning Interactive Real-Robot Action Simulators [24.591694756757278]
  We introduce a novel method, IRASim, to generate realistic videos of a robot arm that executes a given action trajectory. To validate the effectiveness of our method, we create a new benchmark, IRASim Benchmark, based on three real-robot datasets. Results show that IRASim outperforms all the baseline methods and is more preferable in human evaluations.
  arXiv  Detail & Related papers  (2024-06-20T17:50:16Z)
• Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation [8.940998315746684]
  We propose a model-based reinforcement learning (RL) approach for robotic arm end-tasks. We employ Bayesian neural network models to represent, in a probabilistic way, both the belief and information encoded in the dynamic model during exploration. Our experiments show the advantages of our Bayesian model-based RL approach, with similar quality in the results than relevant alternatives.
  arXiv  Detail & Related papers  (2024-04-02T11:44:37Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Active Exploration for Robotic Manipulation [40.39182660794481]
  This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method.
  arXiv  Detail & Related papers  (2022-10-23T18:07:51Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)

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.