Quadrature Oscillation System for Coordinated Motion in Crawling Origami Robot
- URL: http://arxiv.org/abs/2603.23666v1
- Date: Tue, 24 Mar 2026 19:10:19 GMT
- Title: Quadrature Oscillation System for Coordinated Motion in Crawling Origami Robot
- Abstract summary: We develop a quadrature oscillation system capable of generating four square-wave signals a quarter-cycle out of phase.<n>We demonstrate the practicality and value of this oscillation system by designing and constructing an origami crawling robot.
- Score: 4.696356818683829
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Origami-inspired robots offer rapid, accessible design and manufacture with diverse functionalities. In particular, origami robots without conventional electronics have the unique advantage of functioning in extreme environments such as ones with high radiation or large magnetic fields. However, the absence of sophisticated control systems limits these robots to simple autonomous behaviors. In our previous studies, we developed a printable, electronics-free, and self-sustained oscillator that generates simple complementary square-wave signals. Our study presents a quadrature oscillation system capable of generating four square-wave signals a quarter-cycle out of phase, enabling four distinct states. Such control signals are important in various engineering and robotics applications, such as orchestrating limb movements in bio-inspired robots. We demonstrate the practicality and value of this oscillation system by designing and constructing an origami crawling robot that utilizes the quadrature oscillator to achieve coordinated locomotion. Together, the oscillator and robot illustrate the potential for more complex control and functions in origami robotics, paving the way for more electronics-free, rapid-design origami robots with advanced autonomous behaviors.
Related papers
- Aggregating swarms through morphology handling design contingencies: from the sweet spot to a rich expressivity [0.12744523252873352]
We build on a faithful physical model of the self-aligning dynamics of the robots.<n>We show that exploring a range of self-alignment strength allows for a rich expressivity of collective behaviors.
arXiv Detail & Related papers (2026-01-12T14:58:47Z) - RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video [56.9581053843815]
We introduce RobotSeg, a foundation model for robot segmentation in image and video.<n>It addresses the lack of adaptation to articulated robots, reliance on manual prompts, and the need for per-frame training mask annotations.<n>It achieves state-of-the-art performance on both images and videos.
arXiv Detail & Related papers (2025-11-28T07:51:02Z) - Controlling diverse robots by inferring Jacobian fields with deep networks [48.279199537720714]
Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics.<n>We introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field.<n>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) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - 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) - A Transferable Legged Mobile Manipulation Framework Based on Disturbance
Predictive Control [15.044159090957292]
Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot.
We propose a unified framework disturbance predictive control where a reinforcement learning scheme with a latent dynamic adapter is embedded into our proposed low-level controller.
arXiv Detail & Related papers (2022-03-02T14:54:10Z) - 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) - 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) - Populations of Spiking Neurons for Reservoir Computing: Closed Loop
Control of a Compliant Quadruped [64.64924554743982]
We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control.
We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
arXiv Detail & Related papers (2020-04-09T14:32:49Z)
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.