A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

• URL: http://arxiv.org/abs/2007.00463v1
• Date: Wed, 1 Jul 2020 13:02:04 GMT
• Title: A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
• Authors: Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary, Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha
• Abstract summary: We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem. The focus is on producing decisions that can be physically implemented by a robotic loading arm. We show that the RL-based method outperforms state-of-the-art online bin packings in terms of empirical competitive ratio and volume efficiency.
• Score: 7.79020719611004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RL-based method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.

Related papers

• General Geometry-aware Weakly Supervised 3D Object Detection [62.26729317523975]
  A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation.
  arXiv  Detail & Related papers  (2024-07-18T17:52:08Z)
• Neural Packing: from Visual Sensing to Reinforcement Learning [24.35678534893451]
  We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D. It constitutes a full solution pipeline from partial observations of input objects via RGBD sensing and recognition to final box placement, via robotic motion planning, to arrive at a compact packing in a target container.
  arXiv  Detail & Related papers  (2023-10-17T02:42:54Z)
• Learning Physically Realizable Skills for Online Packing of General 3D Shapes [41.27652080050046]
  We study the problem of learning online packing skills for irregular 3D shapes. The goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container. We take physical realizability into account, involving physics dynamics and constraints of a placement.
  arXiv  Detail & Related papers  (2022-12-05T08:23:39Z)
• Planning Irregular Object Packing via Hierarchical Reinforcement Learning [85.64313062912491]
  We propose a deep hierarchical reinforcement learning approach to plan packing sequence and placement for irregular objects. We show that our approach can pack more objects with less time cost than the state-of-the-art packing methods of irregular objects.
  arXiv  Detail & Related papers  (2022-11-17T07:16:37Z)
• OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection [51.153003057515754]
  OPA-3D is a single-stage, end-to-end, Occlusion-Aware Pixel-Wise Aggregation network. It jointly estimates dense scene depth with depth-bounding box residuals and object bounding boxes. It outperforms state-of-the-art methods on the main Car category.
  arXiv  Detail & Related papers  (2022-11-02T14:19:13Z)
• Online 3D Bin Packing Reinforcement Learning Solution with Buffer [1.8060107352742993]
  We present a new reinforcement learning framework for a 3D-BPP solution for improving performance. We implement a model-based RL method adapted from the popular algorithm AlphaGo. Our adaptation is capable of working in single-player and score based environments.
  arXiv  Detail & Related papers  (2022-08-15T11:28:20Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• Learning Practically Feasible Policies for Online 3D Bin Packing [36.33774915391967]
  We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP) We adopt deep reinforcement learning, in particular, the on-policy actor-critic framework, to solve this MDP with constrained action space.
  arXiv  Detail & Related papers  (2021-08-31T08:37:58Z)
• RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection [138.2892824662943]
  A promising solution is to make better use of the synthetic dataset, which consists of CAD object models, to boost the learning on real datasets. Recent work on 3D pre-training exhibits failure when transfer features learned on synthetic objects to other real-world applications. In this work, we put forward a new method called RandomRooms to accomplish this objective.
  arXiv  Detail & Related papers  (2021-08-17T17:56:12Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• Factor Graph based 3D Multi-Object Tracking in Point Clouds [8.411514688735183]
  We propose a novel optimization-based approach that does not rely on explicit and fixed assignments. We demonstrate its performance on the real world KITTI tracking dataset and achieve better results than many state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-08-12T13:34:46Z)

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.