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Bipedal robots demonstrate potential in navigating challenging terrains through dynamic ground contact. However, current frameworks often depend solely on proprioception or use manually designed visual pipelines, which are fragile in…

机器人学 · 计算机科学 2025-08-12 Minku Kim , Brian Acosta , Pratik Chaudhari , Michael Posa

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance…

Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot tasks. Dynamic obstacles may also be interactive, i.e., their behavior varies based on the behavior of other entities.…

机器人学 · 计算机科学 2024-05-21 Baskın Şenbaşlar , Gaurav S. Sukhatme

Stable bipedal walking is a key prerequisite for humanoid robots to reach their potential of being versatile helpers in our everyday environments. Bipedal walking is, however, a complex motion that requires the coordination of many degrees…

机器人学 · 计算机科学 2020-11-06 Marcell Missura , Maren Bennewitz , Sven Behnke

In this paper, we propose a method for selecting the optimal footholds for legged systems. The goal of the proposed method is to find the best foothold for the swing leg on a local elevation map. We apply the Convolutional Neural Network to…

机器人学 · 计算机科学 2022-12-02 D. Belter , J. Bednarek , H. -C. Lin , G. Xin , M. Mistry

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and…

机器人学 · 计算机科学 2020-06-30 Carlos Mastalli , Ioannis Havoutis , Michele Focchi , Darwin G. Caldwell , Claudio Semini

Understanding and predicting pedestrian crossing behavioral intention is crucial for the driving safety of autonomous vehicles. Nonetheless, challenges emerge when using promising images or environmental context masks to extract various…

计算机视觉与模式识别 · 计算机科学 2025-05-13 Chen Xie , Ciyun Lin , Xiaoyu Zheng , Bowen Gong , Antonio M. López

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The…

机器人学 · 计算机科学 2019-09-04 Rahul Tallamraju , Sujit Rajappa , Michael Black , Kamalakar Karlapalem , Aamir Ahmad

We present HumanCM, a one-step human motion prediction framework built upon consistency models. Instead of relying on multi-step denoising as in diffusion-based methods, HumanCM performs efficient single-step generation by learning a…

计算机视觉与模式识别 · 计算机科学 2025-10-24 Liu Haojie , Gao Suixiang

We present an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing methods often depend on heuristics for real-time foothold selection-limiting robustness and adaptability-or rely…

机器人学 · 计算机科学 2025-09-30 Ziyi Zhou , Qian Meng , Hadas Kress-Gazit , Ye Zhao

The contact sequence of humanoid walking consists of single and double support phases (SSP and DSP), and their coordination through proper duration and dynamic transition based on the robot's state is crucial for maintaining walking…

机器人学 · 计算机科学 2026-01-06 Kwanwoo Lee , Gyeongjae Park , Myeong-Ju Kim , Jaeheung Park

Recently, biped robot walking technology has been significantly developed, mainly in the context of a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper,…

机器人学 · 计算机科学 2025-10-15 Bingquan Li , Ning Wang , Tianwei Zhang , Zhicheng He , Yucong Wu

This paper presents an online walking synthesis methodology to enable dynamic and stable walking on constrained footholds for underactuated bipedal robots. Our approach modulates the change of angular momentum about the foot-ground contact…

机器人学 · 计算机科学 2021-09-27 Min Dai , Xiaobin Xiong , Aaron Ames

In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional…

机器人学 · 计算机科学 2020-04-29 Junhyeok Ahn , Jaemin Lee , Luis Sentis

Ensuring safe navigation in complex environments requires accurate real-time traversability assessment and understanding of environmental interactions relative to the robot`s capabilities. Traditional methods, which assume simplified…

机器人学 · 计算机科学 2025-04-30 Pascal Roth , Jonas Frey , Cesar Cadena , Marco Hutter

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…

机器人学 · 计算机科学 2024-10-16 Yunlong Song , Sangbae Kim , Davide Scaramuzza

The complex dynamics of agile robotic legged locomotion requires motion planning to intelligently adjust footstep locations. Often, bipedal footstep and motion planning use mathematically simple models such as the linear inverted pendulum,…

机器人学 · 计算机科学 2022-03-30 Kevin Green , John Warila , Ross L. Hatton , Jonathan Hurst

As legged robots are deployed in industrial and autonomous construction tasks requiring collaborative manipulation, they must handle object manipulation while maintaining stable locomotion. The challenge intensifies in real-world…

机器人学 · 计算机科学 2024-10-16 Mohsen Sombolestan , Quan Nguyen

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an…

Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even minor errors can lead to catastrophic failure. Classical…

机器人学 · 计算机科学 2026-01-13 Min Dai , William D. Compton , Junheng Li , Lizhi Yang , Aaron D. Ames