DittoGym: Learning to Control Soft Shape-Shifting Robots
- URL: http://arxiv.org/abs/2401.13231v2
- Date: Mon, 29 Jan 2024 03:41:34 GMT
- Title: DittoGym: Learning to Control Soft Shape-Shifting Robots
- Authors: Suning Huang and Boyuan Chen and Huazhe Xu and Vincent Sitzmann
- Abstract summary: We explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime.
We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem.
We introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes.
- Score: 30.287452037945542
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robot co-design, where the morphology of a robot is optimized jointly with a
learned policy to solve a specific task, is an emerging area of research. It
holds particular promise for soft robots, which are amenable to novel
manufacturing techniques that can realize learned morphologies and actuators.
Inspired by nature and recent novel robot designs, we propose to go a step
further and explore the novel reconfigurable robots, defined as robots that can
change their morphology within their lifetime. We formalize control of
reconfigurable soft robots as a high-dimensional reinforcement learning (RL)
problem. We unify morphology change, locomotion, and environment interaction in
the same action space, and introduce an appropriate, coarse-to-fine curriculum
that enables us to discover policies that accomplish fine-grained control of
the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark
for reconfigurable soft robots that require fine-grained morphology changes to
accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine
algorithm on DittoGym and demonstrate robots that learn to change their
morphology several times within a sequence, uniquely enabled by our RL
algorithm. More results are available at https://dittogym.github.io.
Related papers
- RoboMorph: Evolving Robot Morphology using Large Language Models [0.5812095716568273]
We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs.
By integrating automatic prompt design and a reinforcement learning based control algorithm, RoboMorph iteratively improves robot designs through feedback loops.
arXiv Detail & Related papers (2024-07-11T16:05:56Z) - HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands.
Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
arXiv Detail & Related papers (2024-03-15T17:45:44Z) - 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) - Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design [40.01142267374432]
Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks.
Previous research has demonstrated the ability to generate robots for various tasks, but these approaches often optimize robots directly in the vast design space.
This paper presents a novel coarse-to-fine method for designing multi-cellular robots.
arXiv Detail & Related papers (2023-11-01T11:56:32Z) - 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) - 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) - 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) - Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots [29.02903745467536]
We propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots.
Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation.
We develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques.
arXiv Detail & Related papers (2022-01-24T18:39:22Z) - Large Scale Distributed Collaborative Unlabeled Motion Planning with
Graph Policy Gradients [122.85280150421175]
We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots.
We employ a graph neural network (GNN) to parameterize policies for the robots.
arXiv Detail & Related papers (2021-02-11T21:57:43Z)
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.