HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling

• URL: http://arxiv.org/abs/2408.01230v1
• Date: Fri, 2 Aug 2024 12:40:01 GMT
• Title: HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling
• Authors: YiFan Hao, Yang Yang, Junru Song, Wei Peng, Weien Zhou, Tingsong Jiang, Wen Yao,
• Abstract summary: HeteroMorpheus is a novel method based on heterogeneous graph Transformer. We demonstrate the superiority of HeteroMorpheus against state-of-the-art methods in the capability of policy generalization.
• Score: 12.771577344846282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of robotic control, designing individual controllers for each robot leads to high computational costs. Universal control policies, applicable across diverse robot morphologies, promise to mitigate this challenge. Predominantly, models based on Graph Neural Networks (GNN) and Transformers are employed, owing to their effectiveness in capturing relational dynamics across a robot's limbs. However, these models typically employ homogeneous graph structures that overlook the functional diversity of different limbs. To bridge this gap, we introduce HeteroMorpheus, a novel method based on heterogeneous graph Transformer. This method uniquely addresses limb heterogeneity, fostering better representation of robot dynamics of various morphologies. Through extensive experiments we demonstrate the superiority of HeteroMorpheus against state-of-the-art methods in the capability of policy generalization, including zero-shot generalization and sample-efficient transfer to unfamiliar robot morphologies.

Related papers

• Evolution and learning in differentiable robots [0.0]
  We use differentiable simulations to rapidly and simultaneously optimize individual neural control of behavior across a large population of candidate body plans. Non-differentiable changes to the mechanical structure of each robot in the population were applied by a genetic algorithm in an outer loop of search. One of the highly differentiable morphologies discovered in simulation was realized as a physical robot and shown to retain its optimized behavior.
  arXiv  Detail & Related papers  (2024-05-23T15:45:43Z)
• DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation, Differentiable Rendering, and Vision-Language Model [72.66465487508556]
  DiffGen is a novel framework that integrates differentiable physics simulation, differentiable rendering, and a vision-language model. It can generate realistic robot demonstrations by minimizing the distance between the embedding of the language instruction and the embedding of the simulated observation. Experiments demonstrate that with DiffGen, we could efficiently and effectively generate robot data with minimal human effort or training time.
  arXiv  Detail & Related papers  (2024-05-12T15:38:17Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)
• Co-evolving morphology and control of soft robots using a single genome [0.0]
  We present a new method to co-evolve morphology and control of robots. Our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster.
  arXiv  Detail & Related papers  (2022-12-22T07:34:31Z)
• A Capability and Skill Model for Heterogeneous Autonomous Robots [69.50862982117127]
  capability modeling is considered a promising approach to semantically model functions provided by different machines. This contribution investigates how to apply and extend capability models from manufacturing to the field of autonomous robots.
  arXiv  Detail & Related papers  (2022-09-22T10:13:55Z)
• MetaMorph: Learning Universal Controllers with Transformers [45.478223199658785]
  In robotics we primarily train a single robot for a single task. modular robot systems now allow for the flexible combination of general-purpose building blocks into task optimized morphologies. We propose MetaMorph, a Transformer based approach to learn a universal controller over a modular robot design space.
  arXiv  Detail & Related papers  (2022-03-22T17:58:31Z)
• REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer [57.045140028275036]
  We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
  arXiv  Detail & Related papers  (2022-02-10T18:50:25Z)

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.