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Human teams can be exceptionally efficient at adapting and collaborating during manipulation tasks using shared mental models. However, the same shared mental models that can be used by humans to perform robust low-level force and motion…

机器人学 · 计算机科学 2017-06-01 Eric C. Townsend , Erich A Mielke , David Wingate , Marc D. Killpack

During co-manipulation involving humans and robots, it is necessary to base robot controllers on human behaviors to achieve comfortable and coordinated movement between the human-robot dyad. In this paper, we describe an experiment between…

机器人学 · 计算机科学 2017-02-03 Erich A. Mielke , Eric C. Townsend , Marc D. Killpack

When a human dyad jointly manipulates an object, they must communicate about their intended motion plans. Some of that collaboration is achieved through the motion of the manipulated object itself, which we call "haptic communication." In…

机器人学 · 计算机科学 2025-09-24 Katherine H. Allen , Chris Rogers , Elaine S. Short

Collaborative manipulation is inherently multimodal, with haptic communication playing a central role. When performed by humans, it involves back-and-forth force exchanges between the participants through which they resolve possible…

机器人学 · 计算机科学 2023-08-21 Zhanibek Rysbek , Ki Hwan Oh , Milos Zefran

With momentum increasing in the use of social robots as long-term assistive and collaborative partners, humans developing social bonds with these artificial agents appears to be inevitable. In human-human dyads, social bonding plays a…

机器人学 · 计算机科学 2023-10-18 Imran Khan

For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to…

机器人学 · 计算机科学 2026-02-17 Kevin Haninger , Luka Peternel

The topic of physical human-robot interaction received a lot of attention from the robotics community because of many promising application domains. However, studying physical interaction between a robot and an external agent, like a human…

This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft…

机器人学 · 计算机科学 2025-04-14 Dallin L. Cordon , Shaden Moss , Marc Killpack , John L. Salmon

Intuitive and efficient physical human-robot collaboration relies on the mutual observability of the human and the robot, i.e. the two entities being able to interpret each other's intentions and actions. This is remedied by a myriad of…

机器人学 · 计算机科学 2022-03-03 Yiming Liu , Raz Leib , William Dudley , Ali Shafti , A. Aldo Faisal , David W. Franklin

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to…

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple…

Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and…

机器人学 · 计算机科学 2023-04-06 Parag Khanna , Mårten Björkman , Christian Smith

A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as…

机器人学 · 计算机科学 2022-04-29 Sunwoo Kim , Maks Sorokin , Jehee Lee , Sehoon Ha

Remote robot manipulation with human control enables applications where safety and environmental constraints are adverse to humans (e.g. underwater, space robotics and disaster response) or the complexity of the task demands human-level…

机器人学 · 计算机科学 2020-11-24 Claudia Pérez-D'Arpino , Rebecca P. Khurshid , Julie A. Shah

Bimanual object manipulation involves multiple visuo-haptic sensory feedbacks arising from the interaction with the environment that are managed from the central nervous system and consequently translated in motor commands. Kinematic…

机器人学 · 计算机科学 2022-11-23 Elisa Galofaro , Erika D'Antonio , Nicola Lotti , Fabrizio Patane' , Maura Casadio , Lorenzo Masia

In the last two decades the scientific community has shown a great interest in understanding and shaping the interaction mechanisms between humans and robots. The interaction implies communication between two dyadic agents and, if the type…

机器人学 · 计算机科学 2019-12-04 Claudia Latella

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However,…

机器人学 · 计算机科学 2021-01-21 Ayumu Sasagawa , Kazuki Fujimoto , Sho Sakaino , Toshiaki Tsuji

The increasing presence of robots alongside humans, such as in human-robot teams in manufacturing, gives rise to research questions about the kind of behaviors people prefer in their robot counterparts. We term actions that support…

机器人学 · 计算机科学 2020-05-05 Shray Bansal , Rhys Newbury , Wesley Chan , Akansel Cosgun , Aimee Allen , Dana Kulić , Tom Drummond , Charles Isbell

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can…

机器人学 · 计算机科学 2019-07-09 Bahareh Abbasi , Ehsan Noohi , Sina Parastegari , Milos Zefran

This work developed collaborative bimanual manipulation for reliable and safe human-robot collaboration, which allows remote and local human operators to work interactively for bimanual tasks. We proposed an optimal motion adaptation to…

机器人学 · 计算机科学 2023-07-19 Ruoshi Wen , Quentin Rouxel , Michael Mistry , Zhibin Li , Carlo Tiseo
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