中文
相关论文

相关论文: Active exploration for body model learning through…

200 篇论文

Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by…

机器人学 · 计算机科学 2021-05-27 Boyuan Chen , Yuhang Hu , Lianfeng Li , Sara Cummings , Hod Lipson

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions…

机器人学 · 计算机科学 2022-05-10 Thomas George Thuruthel , Fumiya Iida

As robots enter the messy human world so the vital matter of safety takes on a fresh complexion with physical contact becoming inevitable and even desirable. We report on an artistic-exploration of how dancers, working as part of a…

Robots learn as they interact with humans. Consider a human teleoperating an assistive robot arm: as the human guides and corrects the arm's motion, the robot gathers information about the human's desired task. But how does the human know…

机器人学 · 计算机科学 2024-04-16 James F. Mullen , Josh Mosier , Sounak Chakrabarti , Anqi Chen , Tyler White , Dylan P. Losey

In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is $\textit{episodic memory}$…

机器人学 · 计算机科学 2024-05-21 Jack Vice , Natalie Ruiz-Sanchez , Pamela K. Douglas , Gita Sukthankar

Unlike robots, humans learn, adapt and perceive their bodies by interacting with the world. Discovering how the brain represents the body and generates actions is of major importance for robotics and artificial intelligence. Here we discuss…

机器人学 · 计算机科学 2021-05-11 Pablo Lanillos , Marcel van Gerven

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require…

机器人学 · 计算机科学 2017-05-18 Jaeyong Sung , J. Kenneth Salisbury , Ashutosh Saxena

This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep…

机器人学 · 计算机科学 2020-03-31 Nathan F. Lepora , John Lloyd

Recent development in developing humanoid robot poses new challenges to human-machine interaction communication. A major challenge is to develop robots that can behave like and interact with human in the most natural way possible. This…

机器人学 · 计算机科学 2014-12-03 Ong Sing Goh , Lance Fung

Dexterous intelligence -- the ability to perform complex interactions with multi-fingered hands -- is a pinnacle of human physical intelligence and emergent higher-order cognitive skills. However, contrary to Moravec's paradox, dexterous…

机器人学 · 计算机科学 2025-07-15 Gagan Khandate

Affective touch in human-robot interaction is shaped not only by emotional intent, but also by robot embodiment, including touch location, physical constraints, and perceived agency or social role. Existing HRI studies typically focus on…

机器人学 · 计算机科学 2026-05-13 Qiaoqiao Ren , Omar Eldardeer , Francesca Cocchella , Rea Francesco , Alessandra Sciutti , Tony Belpaeme

Our sense of touch is essential and permeates in interactions involving natural explorations and affective communications. For instance, we routinely judge the ripeness of fruit at the grocery store, caress the arm of a spouse to offer…

人机交互 · 计算机科学 2022-10-25 Chang Xu

Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it throughout the task. While prior works focus on how the…

High-fidelity physics simulation is essential for scalable robotic learning, but the sim-to-real gap persists, especially for tasks involving complex, dynamic, and discontinuous interactions like physical contacts. Explicit system…

机器人学 · 计算机科学 2026-01-21 Changwei Jing , Jai Krishna Bandi , Jianglong Ye , Yan Duan , Pieter Abbeel , Xiaolong Wang , Sha Yi

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact.…

The increase in available computing power and the Deep Learning revolution have allowed the exploration of new topics and frontiers in Artificial Intelligence research. A new field called Embodied Artificial Intelligence, which places at…

机器人学 · 计算机科学 2025-05-05 Roberto Bigazzi

Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion. In this work, we leverage surprise from mismatches in touch feedback…

Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a…

机器人学 · 计算机科学 2024-03-07 Xuxin Cheng , Yandong Ji , Junming Chen , Ruihan Yang , Ge Yang , Xiaolong Wang

Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of…

机器人学 · 计算机科学 2019-08-20 Changyeob Shin , Peter Walker Ferguson , Sahba Aghajani Pedram , Ji Ma , Erik P. Dutson , Jacob Rosen

While the exploration for embodied AI has spanned multiple decades, it remains a persistent challenge to endow agents with human-level intelligence, including perception, learning, reasoning, decision-making, control, and generalization…

机器人学 · 计算机科学 2024-02-07 Zhiyuan Xu , Kun Wu , Junjie Wen , Jinming Li , Ning Liu , Zhengping Che , Jian Tang