English
Related papers

Related papers: Robust Ladder Climbing with a Quadrupedal Robot

200 papers

Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating base configuration makes them vulnerable to real-world…

Legged robots can traverse challenging terrain, use perception to plan their safe foothold positions, and navigate the environment. Such unique mobility capabilities make these platforms a perfect candidate for scenarios such as search and…

Robotics · Computer Science 2021-07-08 Prathamesh Saraf , Abhishek Sarkar , Arshad Javed

In this paper, we present a complete description of the hardware design and control architecture of our custom built quadruped robot, called the `Stoch'. Our goal is to realize a robust, modular, and a reliable quadrupedal platform, using…

Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. To acquire comprehensive exteroceptive information, prior studies have employed motion…

Robotics · Computer Science 2025-03-04 Ruiqi Yu , Qianshi Wang , Yizhen Wang , Zhicheng Wang , Jun Wu , Qiuguo Zhu

Salamander-like quadruped robots are designed inspired by the skeletal structure of their biological counterparts. However, existing controllers cannot fully exploit these morphological features and largely rely on predefined gait patterns…

Robotics · Computer Science 2025-11-12 Zhiang Liu , Yang Liu , Yongchun Fang , Xian Guo

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative…

Robotics · Computer Science 2024-06-03 Fan Shi , Chong Zhang , Takahiro Miki , Joonho Lee , Marco Hutter , Stelian Coros

Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting…

Robotics · Computer Science 2025-06-24 Joseph Humphreys , Chengxu Zhou

Motivated by a high demand for automated inspection of civil infrastructure, this work presents a new design and development of a tank-like robot for structural health monitoring. Unlike most existing magnetic wheeled mobile robot designs,…

Robotics · Computer Science 2018-09-18 Son Thanh Nguyen , Hung Manh La

Legged robots have achieved impressive feats in dynamic locomotion in challenging unstructured terrain. However, in entertainment applications, the design and control of these robots face additional challenges in appealing to human…

Advancing the dynamic loco-manipulation capabilities of quadruped robots in complex terrains is crucial for performing diverse tasks. Specifically, dynamic ball manipulation in rugged environments presents two key challenges. The first is…

Robotics · Computer Science 2025-04-22 Dongjie Zhu , Zhuo Yang , Tianhang Wu , Luzhou Ge , Xuesong Li , Qi Liu , Xiang Li

Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (\textit{RL-ATR}), inspired by…

Robotics · Computer Science 2026-02-10 Minsung Yoon , Sung-Eui Yoon

This work explores the potential of using differentiable simulation for learning quadruped locomotion. Differentiable simulation promises fast convergence and stable training by computing low-variance first-order gradients using robot…

Robotics · Computer Science 2024-10-16 Yunlong Song , Sangbae Kim , Davide Scaramuzza

Humanoid robots have received significant research interests and advancements in recent years. Despite many successes, due to their morphology, dynamics and limitation of control policy, humanoid robots are prone to fall as compared to…

Robotics · Computer Science 2025-12-02 Diyuan Shi , Shangke Lyu , Donglin Wang

This paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot `Olympus'. Separate policies are developed for vertical and horizontal jumps, leveraging a…

Robotics · Computer Science 2025-10-29 Jørgen Anker Olsen , Lars Rønhaug Pettersen , Kostas Alexis

Developing agile behaviors for legged robots remains a challenging problem. While deep reinforcement learning is a promising approach, learning truly agile behaviors typically requires tedious reward shaping and careful curriculum design.…

Robotics · Computer Science 2020-11-12 Atil Iscen , George Yu , Alejandro Escontrela , Deepali Jain , Jie Tan , Ken Caluwaerts

Multi-legged robots offer enhanced stability in complex terrains, yet autonomously learning natural and robust motions in such environments remains challenging. Drawing inspiration from animals' progressive learning patterns, from simple to…

Robotics · Computer Science 2024-01-24 Yinghui Li , Jinze Wu , Xin Liu , Weizhong Guo , Yufei Xue

Unprecedented possibilities of quadruped robots have driven much research on the technical aspects of these robots. However, the social perception and acceptability of quadruped robots so far remain poorly understood. This work investigates…

Robotics · Computer Science 2024-03-11 Nanami Hashimoto , Emma Hagens , Arkady Zgonnikov , Maria Luce Lupetti

The heavy-load legged robot has strong load carrying capacity and can adapt to various unstructured terrains. But the large weight results in higher requirements for motion stability and environmental perception ability. In order to utilize…

Robotics · Computer Science 2025-07-31 Ze Fu , Yinghui Li , Weizhong Guo

Amphibious legged robots inspired by salamanders are promising in applications in complex amphibious environments. However, despite the significant success of training controllers that achieve diverse locomotion behaviors in conventional…

Robotics · Computer Science 2026-03-18 Mengze Tian , Qiyuan Fu , Chuanfang Ning , Javier Jia Jie Pey , Auke Ijspeert