Scale invariant robot behavior with fractals
- URL: http://arxiv.org/abs/2103.04876v1
- Date: Mon, 8 Mar 2021 16:27:07 GMT
- Title: Scale invariant robot behavior with fractals
- Authors: Sam Kriegman, Amir Mohammadi Nasab, Douglas Blackiston, Hannah Steele,
Michael Levin, Rebecca Kramer-Bottiglio, Josh Bongard
- Abstract summary: Self similar structures in nature often exhibit self similar behavior at different scales.
We show that there are robot designs that exhibit a desired behavior at a small size scale, and if copies of that robot are attached together to realize the same design at higher scales, those larger robots exhibit similar behavior.
- Score: 1.593222804814135
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Robots deployed at orders of magnitude different size scales, and that retain
the same desired behavior at any of those scales, would greatly expand the
environments in which the robots could operate. However it is currently not
known whether such robots exist, and, if they do, how to design them. Since
self similar structures in nature often exhibit self similar behavior at
different scales, we hypothesize that there may exist robot designs that have
the same property. Here we demonstrate that this is indeed the case for some,
but not all, modular soft robots: there are robot designs that exhibit a
desired behavior at a small size scale, and if copies of that robot are
attached together to realize the same design at higher scales, those larger
robots exhibit similar behavior. We show how to find such designs in simulation
using an evolutionary algorithm. Further, when fractal attachment is not
assumed and attachment geometries must thus be evolved along with the design of
the base robot unit, scale invariant behavior is not achieved, demonstrating
that structural self similarity, when combined with appropriate designs, is a
useful path to realizing scale invariant robot behavior. We validate our
findings by demonstrating successful transferal of self similar structure and
behavior to pneumatically-controlled soft robots. Finally, we show that biobots
can spontaneously exhibit self similar attachment geometries, thereby
suggesting that self similar behavior via self similar structure may be
realizable across a wide range of robot platforms in future.
Related papers
- Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
Differentiable simulation has become a powerful tool for system identification.
Our approach calibrates object properties by using information from the robot, without relying on data from the object itself.
We demonstrate the effectiveness of our method on a low-cost robotic platform.
arXiv Detail & Related papers (2024-10-04T20:48:38Z) - No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots [0.24554686192257422]
We show that intelligent behavior can be created without a separate and explicit brain for robot control.
Specifically, we show that adaptive and complex behavior can be created in voxel-based virtual soft robots by using simple reactive materials.
arXiv Detail & Related papers (2024-07-23T16:20:36Z) - 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) - 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) - Singing the Body Electric: The Impact of Robot Embodiment on User
Expectations [7.408858358967414]
Users develop mental models of robots to conceptualize what kind of interactions they can have with those robots.
conceptualizations are often formed before interactions with the robot and are based only on observing the robot's physical design.
We propose to use multimodal features of robot embodiments to predict what kinds of expectations users will have about a given robot's social and physical capabilities.
arXiv Detail & Related papers (2024-01-13T04:42:48Z) - Correspondence learning between morphologically different robots via
task demonstrations [2.1374208474242815]
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.
arXiv Detail & Related papers (2023-10-20T12:42:06Z) - Robot Learning with Sensorimotor Pre-training [98.7755895548928]
We present a self-supervised sensorimotor pre-training approach for robotics.
Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens.
We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
arXiv Detail & Related papers (2023-06-16T17:58:10Z) - Efficient automatic design of robots [43.968830087704035]
We show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer.
Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form.
This advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.
arXiv Detail & Related papers (2023-06-05T21:30:52Z) - 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) - 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) - Cross Domain Robot Imitation with Invariant Representation [32.1735585546968]
Cross domain imitation learning (CDIL) is a challenging task in robotics.
We introduce an imitation learning algorithm based on invariant representation.
We show that our method is able to learn similar representations for different robots with similar behaviors.
arXiv Detail & Related papers (2021-09-13T13:05:35Z)
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.