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相关论文: Data-driven Adaptation for Robust Bipedal Locomoti…

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Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based…

机器人学 · 计算机科学 2025-01-30 Giulio Turrisi , Valerio Modugno , Lorenzo Amatucci , Dimitrios Kanoulas , Claudio Semini

A safety-critical measure of legged locomotion performance is a robot's ability to track its desired time-varying position trajectory in an environment, which is herein termed as "global-position tracking". This paper introduces a nonlinear…

机器人学 · 计算机科学 2021-08-10 Yan Gu , Yuan Gao , Bin Yao , C. S. George Lee

In this paper we exploit some interesting properties of a class of bipedal robots which have an inertial disc. One of this properties is the ability to control every position and speed except for the disc position. The proposed control is…

Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging…

机器人学 · 计算机科学 2020-02-07 Marko Bjelonic , Prajish K. Sankar , C. Dario Bellicoso , Heike Vallery , Marco Hutter

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast…

机器人学 · 计算机科学 2020-07-31 Xingyou Song , Yuxiang Yang , Krzysztof Choromanski , Ken Caluwaerts , Wenbo Gao , Chelsea Finn , Jie Tan

Model-based control usually relies on an accurate model, which is often obtained from CAD and actuator models. The more accurate the model the better the control performance. However, in bipedal robots that demonstrate high agility actions,…

机器人学 · 计算机科学 2021-07-27 Bolun Dai

We propose an online motion planner for legged robot locomotion with the primary objective of achieving energy efficiency. The conceptual idea is to leverage a placement set of footstep positions based on the robot's body position to…

机器人学 · 计算机科学 2025-06-25 Alexander Schperberg , Marcel Menner , Stefano Di Cairano

Safe path and gait planning are essential for bipedal robots to navigate complex real-world environments. The prevailing approaches often plan the path and gait separately in a hierarchical fashion, potentially resulting in unsafe movements…

机器人学 · 计算机科学 2024-03-27 Chengyang Peng , Victor Paredes , Ayonga Hereid

Planetary exploration missions require robots capable of navigating extreme and unknown environments. While wheeled rovers have dominated past missions, their mobility is limited to traversable surfaces. Legged robots, especially…

This paper presents a novel approach that combines the advantages of both model-based and learning-based frameworks to achieve robust locomotion. The residual modules are integrated with each corresponding part of the model-based framework,…

机器人学 · 计算机科学 2025-07-25 Min-Gyu Kim , Dongyun Kang , Hajun Kim , Hae-Won Park

In this paper, we propose a novel framework on force-and-moment-based Model Predictive Control (MPC) for dynamic legged robots. Specifically, we present a formulation of MPC designed for 10 degree-of-freedom (DoF) bipedal robots using…

机器人学 · 计算机科学 2021-10-07 Junheng Li , Quan Nguyen

Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robots remains unverified. This paper explores the adaptation of…

In this chapter we will highlight our experimental studies on natural human walking analysis and introduce a biologically inspired design for simple bipedal locomotion system of humanoid robots. Inspiration comes directly from human walking…

机器人学 · 计算机科学 2012-12-11 Nizar Rokbani , Boudour Ammar Cherif , Adel M. Alimi

We present a highly reactive controller which enables bipedal robots to blindly walk over various kinds of uneven terrains while resisting pushes. The high level motion planner does fast online optimization for footstep locations and Center…

机器人学 · 计算机科学 2021-09-21 Ke Wang , Hengyi Fei , Petar Kormushev

This work introduces a hierarchical strategy for terrain-aware bipedal locomotion that integrates reduced-dimensional perceptual representations to enhance reinforcement learning (RL)-based high-level (HL) policies for real-time gait…

机器人学 · 计算机科学 2025-12-16 Guillermo A. Castillo , Himanshu Lodha , Ayonga Hereid

Quadruped robots are often designed with rigid feet to simplify control and maintain stable contact during locomotion. While this approach is straightforward, it limits the ability of the legs to absorb impact forces and reuse stored…

机器人学 · 计算机科学 2026-05-15 Pramod Pal , Shishir Kolathaya , Ashitava Ghosal

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system,…

机器人学 · 计算机科学 2022-10-17 Jee-eun Lee , Jaemin Lee , Tirthankar Bandyopadhyay , Luis Sentis

In this paper, a simple trajectory generation method for biped walking is proposed. The dynamic model of the five link bipedal robot is first reduced using several biologically inspired assumptions. A sinusoidal curve is then imposed to the…

机器人学 · 计算机科学 2014-05-14 Yosra Arous , Olfa Boubaker

Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian…

机器人学 · 计算机科学 2018-10-11 Rika Antonova , Akshara Rai , Christopher G. Atkeson

Humans are efficient, yet expressive in their motion. Human walking behaviors can be used to walk across a great variety of surfaces without falling and to communicate internal state to other humans through variable gait styles. This…

机器人学 · 计算机科学 2019-09-20 Umer Huzaifa , Catherine Maguire , Amy LaViers
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