Correspondence learning between morphologically different robots via task demonstrations

• URL: http://arxiv.org/abs/2310.13458v3
• Date: Tue, 21 Nov 2023 19:33:52 GMT
• Title: Correspondence learning between morphologically different robots via task demonstrations
• Authors: Hakan Aktas, Yukie Nagai, Minoru Asada, Erhan Oztop, Emre Ugur
• Abstract summary: We propose a method to learn correspondences among two or more robots that may have different morphologies. A fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework. We provide a proof-of-the-concept realization of correspondence learning between a real manipulator robot and a simulated mobile robot.
• Score: 2.1374208474242815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We observe a large variety of robots in terms of their bodies, sensors, and actuators. Given the commonalities in the skill sets, teaching each skill to each different robot independently is inefficient and not scalable when the large variety in the robotic landscape is considered. If we can learn the correspondences between the sensorimotor spaces of different robots, we can expect a skill that is learned in one robot can be more directly and easily transferred to other robots. In this paper, we propose a method to learn correspondences among two or more robots that may have different morphologies. To be specific, besides robots with similar morphologies with different degrees of freedom, we show that a fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework. To set up the correspondence among the robots considered, an initial base task is demonstrated to the robots to achieve the same goal. Then, a common latent representation is learned along with the individual robot policies for achieving the goal. After the initial learning stage, the observation of a new task execution by one robot becomes sufficient to generate a latent space representation pertaining to the other robots to achieve the same task. We verified our system in a set of experiments where the correspondence between robots is learned (1) when the robots need to follow the same paths to achieve the same task, (2) when the robots need to follow different trajectories to achieve the same task, and (3) when complexities of the required sensorimotor trajectories are different for the robots. We also provide a proof-of-the-concept realization of correspondence learning between a real manipulator robot and a simulated mobile robot.

Related papers

• 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)
• Unifying 3D Representation and Control of Diverse Robots with a Single Camera [48.279199537720714]
  We introduce Neural Jacobian Fields, an architecture that autonomously learns to model and control robots from vision alone. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
  We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots. We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
  arXiv  Detail & Related papers  (2022-09-12T15:14:32Z)
• Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation [104.5440430194206]
  This work investigates the perception of object manipulations performed with a communicative intent by two robots. We designed the robots' movements to communicate carefulness or not during the transportation of objects.
  arXiv  Detail & Related papers  (2022-08-03T13:26:52Z)
• 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)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• Enabling the Sense of Self in a Dual-Arm Robot [2.741266294612776]
  We present a neural network architecture that enables a dual-arm robot to get a sense of itself in an environment. We demonstrate experimentally that a robot can distinguish itself with an accuracy of 88.7% on average in cluttered environmental settings.
  arXiv  Detail & Related papers  (2020-11-13T17:25:07Z)
• Learning Locomotion Skills in Evolvable Robots [10.167123492952694]
  We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.
  arXiv  Detail & Related papers  (2020-10-19T14:01:50Z)

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.