Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning
- URL: http://arxiv.org/abs/2107.02955v1
- Date: Wed, 7 Jul 2021 00:34:23 GMT
- Title: Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning
- Authors: Taehei Kim, Sung-Hee Lee
- Abstract summary: We present a novel reinforcement learning framework for learning locomotion on non-rigid dynamic terrains.
A trained robot with 55cm base length can walk on terrain that can sink up to 5cm.
We show the effectiveness of our method by training the robot with various terrain conditions.
- Score: 10.729374293332281
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Legged robots need to be capable of walking on diverse terrain conditions. In
this paper, we present a novel reinforcement learning framework for learning
locomotion on non-rigid dynamic terrains. Specifically, our framework can
generate quadruped locomotion on flat elastic terrain that consists of a matrix
of tiles moving up and down passively when pushed by the robot's feet. A
trained robot with 55cm base length can walk on terrain that can sink up to
5cm. We propose a set of observation and reward terms that enable this
locomotion; in which we found that it is crucial to include the end-effector
history and end-effector velocity terms into observation. We show the
effectiveness of our method by training the robot with various terrain
conditions.
Related papers
- Learning Humanoid Locomotion over Challenging Terrain [84.35038297708485]
We present a learning-based approach for blind humanoid locomotion capable of traversing challenging natural and man-made terrains.
Our model is first pre-trained on a dataset of flat-ground trajectories with sequence modeling, and then fine-tuned on uneven terrain using reinforcement learning.
We evaluate our model on a real humanoid robot across a variety of terrains, including rough, deformable, and sloped surfaces.
arXiv Detail & Related papers (2024-10-04T17:57:09Z) - Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots.
Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism.
We present two methods for tackling the benchmark.
arXiv Detail & Related papers (2023-05-24T02:49:43Z) - Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion [34.33972863987201]
We train quadruped robots to use the front legs to climb walls, press buttons, and perform object interaction in the real world.
These skills are trained in simulation using curriculum and transferred to the real world using our proposed sim2real variant.
We evaluate our method in both simulation and real-world showing successful executions of both short as well as long-range tasks.
arXiv Detail & Related papers (2023-03-20T17:59:58Z) - Legged Locomotion in Challenging Terrains using Egocentric Vision [70.37554680771322]
We present the first end-to-end locomotion system capable of traversing stairs, curbs, stepping stones, and gaps.
We show this result on a medium-sized quadruped robot using a single front-facing depth camera.
arXiv Detail & Related papers (2022-11-14T18:59:58Z) - Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement
Learning [18.873152528330063]
We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world.
Soccer goalkeeping using quadrupeds is a challenging problem, that combines highly dynamic locomotion with precise and fast non-prehensile object (ball) manipulation.
We deploy the proposed framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness of our framework for various agile interceptions of a fast-moving ball in the real world.
arXiv Detail & Related papers (2022-10-10T04:54:55Z) - Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills
using a Quadrupedal Robot [76.04391023228081]
We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning.
We propose a hierarchical framework that leverages deep reinforcement learning to train a robust motion control policy.
We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.
arXiv Detail & Related papers (2022-08-01T22:34:51Z) - Learning Semantics-Aware Locomotion Skills from Human Demonstration [35.996425893483796]
We present a framework that learns semantics-aware locomotion skills from perception for quadrupedal robots.
Our framework learns to adjust the speed and gait of the robot based on perceived terrain semantics, and enables the robot to walk over 6km without failure.
arXiv Detail & Related papers (2022-06-27T21:08:03Z) - Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot.
We show superior performance compared to wheeled robot (LoCoBot) baselines.
We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
arXiv Detail & Related papers (2021-12-03T18:59:59Z) - Robust Quadruped Jumping via Deep Reinforcement Learning [10.095966161524043]
In this paper, we consider jumping varying distances and heights for a quadrupedal robot in noisy environments.
We propose a framework using deep reinforcement learning that leverages and augments the complex solution of nonlinear trajectory optimization for quadrupedal jumping.
We demonstrate robustness of foot disturbances of up to 6 cm in height, or 33% of the robot's nominal standing height, while jumping 2x the body length in distance.
arXiv Detail & Related papers (2020-11-13T19:04:24Z) - Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
Legged locomotion can dramatically expand the operational domains of robotics.
Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes.
Here we present a radically robust controller for legged locomotion in challenging natural environments.
arXiv Detail & Related papers (2020-10-21T19:11:20Z)
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.