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Traversability estimation in off-road terrains is an essential procedure for autonomous navigation. However, creating reliable labels for complex interactions between the robot and the surface is still a challenging problem in…

机器人学 · 计算机科学 2024-09-17 Qiumin Zhu , Zhen Sun , Songpengcheng Xia , Guoqing Liu , Kehui Ma , Ling Pei , Zheng Gong , Cheng Jin

Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances,…

机器人学 · 计算机科学 2025-06-12 Junzhe He , Chong Zhang , Fabian Jenelten , Ruben Grandia , Moritz BÄcher , Marco Hutter

Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental…

机器人学 · 计算机科学 2024-07-16 Yi Cheng , Hang Liu , Guoping Pan , Linqi Ye , Houde Liu , Bin Liang

Autonomous navigation in extreme mountainous terrains poses challenges due to the presence of mobility-stressing elements and undulating surfaces, making it particularly difficult compared to conventional off-road driving scenarios. In such…

机器人学 · 计算机科学 2024-04-16 Se-Wook Yoo , E In Son , Seung-Woo Seo

Autonomous navigation in unstructured environments requires robots to assess terrain difficulty in real-time and plan paths that balance efficiency with safety. This thesis presents a traversability-aware navigation framework for the M4…

机器人学 · 计算机科学 2025-12-16 Hrigved Mahesh Suryawanshi

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into under-explored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion:…

机器人学 · 计算机科学 2022-01-21 Takahiro Miki , Joonho Lee , Jemin Hwangbo , Lorenz Wellhausen , Vladlen Koltun , Marco Hutter

Robots are increasingly integrated across industries, particularly in healthcare. However, many valuable applications for quadrupedal robots remain overlooked. This research explores the effectiveness of three reinforcement learning…

机器人学 · 计算机科学 2025-07-18 Emma M. A. Harrison

The enhanced mobility brought by legged locomotion empowers quadrupedal robots to navigate through complex and unstructured environments. However, optimizing agile locomotion while accounting for the varying energy costs of traversing…

This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for learning locomotion-related tasks. The…

Building autonomous mobile robots (AMRs) with optimized efficiency and adaptive capabilities-able to respond to changing task demands and dynamic environments-is a strongly desired goal for advancing construction robotics. Such robots can…

机器人学 · 计算机科学 2025-09-16 Xiaoshan Zhou , Carol C. Menassa , Vineet R. Kamat

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully…

机器人学 · 计算机科学 2022-07-26 Zipeng Fu , Ashish Kumar , Ananye Agarwal , Haozhi Qi , Jitendra Malik , Deepak Pathak

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the…

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight…

机器人学 · 计算机科学 2021-02-10 Maozhen Wang , Rui Luo , Aykut Ozgun Onol , Taskin Padir

Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task…

机器人学 · 计算机科学 2023-09-25 Bhabaranjan Panigrahi , Amir Hossain Raj , Mohammad Nazeri , Xuesu Xiao

This paper addresses the challenge of active perception within autonomous navigation in complex, unknown environments. Revisiting the foundational principles of active perception, we introduce an end-to-end reinforcement learning framework…

机器人学 · 计算机科学 2026-02-03 Grzegorz Malczyk , Mihir Kulkarni , Kostas Alexis

Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to create informative labels to learn a model in a supervised…

Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of…

机器人学 · 计算机科学 2021-08-31 Russell Buchanan , Marco Camurri , Maurice Fallon

Achieving safe quadrupedal locomotion in real-world environments has attracted much attention in recent years. When walking over uneven terrain, achieving reliable estimation and realising safety-critical control based on the obtained…

机器人学 · 计算机科学 2026-03-11 Peiyu Yang , Jiatao Ding , Wei Pan , Claudio Semini , Cosimo Della Santina

Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating…

机器人学 · 计算机科学 2023-02-15 Jonas Frey , David Hoeller , Shehryar Khattak , Marco Hutter

Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major…

机器人学 · 计算机科学 2024-09-30 Junli Ren , Yikai Liu , Yingru Dai , Junfeng Long , Guijin Wang
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