Related papers: Advances, challenges, and opportunities for legged robots

Advances, challenges, and opportunities for legged robots

URL: http://arxiv.org/abs/2607.28952v1
Date: Fri, 31 Jul 2026 02:16:54 GMT
Title: Advances, challenges, and opportunities for legged robots
Abstract summary: Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines.<n>We assess the current capabilities of these systems along hardware, locomotion, autonomy, data, and applications.<n>We provide an outlook on the future of legged robots, exploring their ethical considerations, economic potential, policy implications, and broader societal effects.
Score: 31.178760547666855
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how they can enable scientific discovery, we assess the current capabilities of these systems along hardware, locomotion, autonomy, data, and applications. We identify recent advances and key open challenges that must be overcome to enable widespread adoption and new use cases for legged robots. Last, we provide an outlook on the future of legged robots, exploring their ethical considerations, economic potential, policy implications, and broader societal effects.

Related papers

A roadmap for AI in robotics [55.87087746398059]
We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
arXiv Detail & Related papers (2025-07-26T15:18:28Z)
Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z)
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z)
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
arXiv Detail & Related papers (2022-12-08T15:56:13Z)
Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together [68.44697646919515]
This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
arXiv Detail & Related papers (2022-02-03T10:04:26Z)
A Survey on Human-aware Robot Navigation [22.926878248788558]
This paper is concerned with the navigation aspect of a socially-compliant robot. It provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.
arXiv Detail & Related papers (2021-06-22T10:09:25Z)
Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception [2.1067139116005595]
We discuss how neuroscience findings open up opportunities to improve current estimation and control algorithms in robotics. This paper summarizes some experiments and lessons learned from developing such a computational model on real embodied platforms.
arXiv Detail & Related papers (2021-05-10T10:59:38Z)
From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation [3.3511723893430476]
We argue that social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. Our proposed framework consists of two components: textitlearning which incorporates social context into the learning process to account for safety and comfort, and textitrelearning to detect and correct potentially harmful outcomes before the onset.
arXiv Detail & Related papers (2021-01-07T17:42:35Z)
OpenBot: Turning Smartphones into Robots [95.94432031144716]
Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities. We propose to leverage smartphones to equip robots with extensive sensor suites, powerful computational abilities, state-of-the-art communication channels, and access to a thriving software ecosystem. We design a small electric vehicle that costs $50 and serves as a robot body for standard Android smartphones.
arXiv Detail & Related papers (2020-08-24T18:04:50Z)
A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives [44.45953630612019]
Recent success of machine learning in many domains has been overwhelming. We will give a broad overview of behaviors that have been learned and used on real robots.
arXiv Detail & Related papers (2019-06-05T07:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.