Learning to Design and Construct Bridge without Blueprint

• URL: http://arxiv.org/abs/2108.02439v1
• Date: Thu, 5 Aug 2021 08:17:22 GMT
• Title: Learning to Design and Construct Bridge without Blueprint
• Authors: Yunfei Li, Tao Kong, Lei Li, Yifeng Li and Yi Wu
• Abstract summary: We study a new challenging assembly task, designing and constructing a bridge without a blueprint. In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design. At the high level, the system learns a bridge blueprint policy in a physical simulator using deep reinforcement learning and curriculum learning. For low-level control, we implement a motion-planning-based policy for real-robot motion control, which can be directly combined with a trained blueprint policy for real-world bridge construction without tuning.
• Score: 20.524052738716435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous assembly has been a desired functionality of many intelligent robot systems. We study a new challenging assembly task, designing and constructing a bridge without a blueprint. In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design. In this paper, we propose a bi-level approach to tackle this task. At the high level, the system learns a bridge blueprint policy in a physical simulator using deep reinforcement learning and curriculum learning. A policy is represented as an attention-based neural network with object-centric input, which enables generalization to different numbers of blocks and cliff widths. For low-level control, we implement a motion-planning-based policy for real-robot motion control, which can be directly combined with a trained blueprint policy for real-world bridge construction without tuning. In our field study, our bi-level robot system demonstrates the capability of manipulating blocks to construct a diverse set of bridges with different architectures.

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Solving Multi-Goal Robotic Tasks with Decision Transformer [0.0]
  We introduce a novel adaptation of the decision transformer architecture for offline multi-goal reinforcement learning in robotics. Our approach integrates goal-specific information into the decision transformer, allowing it to handle complex tasks in an offline setting.
  arXiv  Detail & Related papers  (2024-10-08T20:35:30Z)
• Body Transformer: Leveraging Robot Embodiment for Policy Learning [51.531793239586165]
  Body Transformer (BoT) is an architecture that leverages the robot embodiment by providing an inductive bias that guides the learning process. We represent the robot body as a graph of sensors and actuators, and rely on masked attention to pool information throughout the architecture. The resulting architecture outperforms the vanilla transformer, as well as the classical multilayer perceptron, in terms of task completion, scaling properties, and computational efficiency.
  arXiv  Detail & Related papers  (2024-08-12T17:31:28Z)
• An attempt to generate new bridge types from latent space of generative adversarial network [2.05750372679553]
  Symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge are used. Based on Python programming language, and Keras deep learning platform framework, generative adversarial network is constructed and trained.
  arXiv  Detail & Related papers  (2024-01-01T08:46:29Z)
• Polybot: Training One Policy Across Robots While Embracing Variability [70.74462430582163]
  We propose a set of key design decisions to train a single policy for deployment on multiple robotic platforms. Our framework first aligns the observation and action spaces of our policy across embodiments via utilizing wrist cameras. We evaluate our method on a dataset collected over 60 hours spanning 6 tasks and 3 robots with varying joint configurations and sizes.
  arXiv  Detail & Related papers  (2023-07-07T17:21:16Z)
• Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets [30.993174896902357]
  Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint? It is a challenging task with long horizon and sparse reward -- the robot has to figure out physically stable design schemes and feasible actions to manipulate and transport blocks. In this paper, we propose a hierarchical approach for this problem. It consists of a reinforcement-learning designer to propose high-level building instructions and a motion-planning-based action generator to manipulate blocks at the low level.
  arXiv  Detail & Related papers  (2022-04-12T03:45:48Z)
• Policy Architectures for Compositional Generalization in Control [71.61675703776628]
  We introduce a framework for modeling entity-based compositional structure in tasks. Our policies are flexible and can be trained end-to-end without requiring any action primitives.
  arXiv  Detail & Related papers  (2022-03-10T06:44:24Z)
• Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery [34.25379651790627]
  We tackle the problem of building arbitrary, predefined target structures entirely from scratch using a set of Tetris-like building blocks and a robotic manipulator. Our novel hierarchical approach aims at efficiently decomposing the overall task into three feasible levels that benefit mutually from each other.
  arXiv  Detail & Related papers  (2022-03-08T14:44:51Z)
• Cooperative Task and Motion Planning for Multi-Arm Assembly Systems [32.56644393804845]
  Multi-robot assembly systems are becoming increasingly appealing in manufacturing. Planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging. We present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures.
  arXiv  Detail & Related papers  (2022-03-04T18:12:49Z)
• Simultaneous Navigation and Construction Benchmarking Environments [73.0706832393065]
  We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major robot vision and learning challenge is how to exactly achieve the design without GPS. We benchmark the performance of a handcrafted policy with basic localization and planning, and state-of-the-art deep reinforcement learning methods.
  arXiv  Detail & Related papers  (2021-03-31T00:05:54Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)

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.