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相关论文: A Contact-Driven Framework for Manipulating in the…

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Robotic manipulation research has investigated contact-rich problems and strategies that require robots to intentionally collide with their environment, to accomplish tasks that cannot be handled by traditional collision-free solutions. By…

机器人学 · 计算机科学 2025-09-15 Kejia Ren , Gaotian Wang , Andrew S. Morgan , Kaiyu Hang

In this work, we build on our method for manipulating unknown objects via contact configuration regulation: the estimation and control of the location, geometry, and mode of all contacts between the robot, object, and environment. We…

机器人学 · 计算机科学 2023-10-03 Orion Taylor , Neel Doshi , Alberto Rodriguez

Real-world object manipulation has been commonly challenged by physical uncertainties and perception limitations. Being an effective strategy, while caging configuration-based manipulation frameworks have successfully provided robust…

机器人学 · 计算机科学 2025-05-05 Gaotian Wang , Kejia Ren , Andrew S. Morgan , Kaiyu Hang

Many robot manipulation tasks require the robot to make and break contact with objects and surfaces. The dynamics of such changing-contact robot manipulation tasks are discontinuous when contact is made or broken, and continuous elsewhere.…

机器人学 · 计算机科学 2021-06-22 Saif Sidhik , Mohan Sridharan , Dirk Ruiken

Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions…

机器人学 · 计算机科学 2022-08-05 Kejia Ren , Lydia E. Kavraki , Kaiyu Hang

The integration of collaborative robots into industrial environments has improved productivity, but has also highlighted significant challenges related to operator safety and ergonomics. This paper proposes an innovative framework that…

机器人学 · 计算机科学 2025-04-15 Francesco Iodice , Elena De Momi , Arash Ajoudani

Glovebox decommissioning tasks usually require manipulating relatively heavy objects in a highly constrained environment. Thus, contact with the surroundings becomes inevitable. In order to allow the robot to interact with the environment…

机器人学 · 计算机科学 2018-07-12 Aykut Onol , Philip Long , Taskin Padir

Many manipulation tasks require the robot to control the contact between a grasped compliant tool and the environment, e.g. scraping a frying pan with a spatula. However, modeling tool-environment interaction is difficult, especially when…

机器人学 · 计算机科学 2022-10-11 Mark Van der Merwe , Dmitry Berenson , Nima Fazeli

We describe a framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dynamics of such tasks make it difficult to construct and use a…

机器人学 · 计算机科学 2021-11-16 Saif Sidhik , Mohan Sridharan , Dirk Ruiken

Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible…

机器人学 · 计算机科学 2025-09-03 Nils Dengler , Jesper Mücke , Rohit Menon , Maren Bennewitz

We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking…

机器人学 · 计算机科学 2021-10-25 Alp Aydinoglu , Philip Sieg , Victor M. Preciado , Michael Posa

The growing presence of service robots in human-centric environments, such as warehouses, demands seamless and intuitive human-robot collaboration. In this paper, we propose a collaborative shelf-picking framework that combines multimodal…

机器人学 · 计算机科学 2025-04-10 Abhinav Pathak , Kalaichelvi Venkatesan , Tarek Taha , Rajkumar Muthusamy

Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors and inaccurate motor control. To combat these factors and…

机器人学 · 计算机科学 2020-10-06 Lin Shao , Toki Migimatsu , Jeannette Bohg

Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact…

机器人学 · 计算机科学 2025-11-21 Zili Tang , Ying Zhang , Meng Guo

Non-flat surfaces pose difficulties for robots operating in unstructured environments. Reconstructions of uneven surfaces may only be partially possible due to non-compliant end-effectors and limitations on vision systems such as…

Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting…

Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since unexpected dangerous collisions might happen when the RL…

机器人学 · 计算机科学 2025-05-27 Xiang Zhu , Shucheng Kang , Jianyu Chen

In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact between the robot bodies and the environment. However, in…

机器人学 · 计算机科学 2022-11-16 Xinghao Zhu , Wenzhao Lian , Bodi Yuan , C. Daniel Freeman , Masayoshi Tomizuka

Robotic manipulation demands precise control over both contact forces and motion trajectories. While force control is essential for achieving compliant interaction and high-frequency adaptation, it is limited to operations in close…

机器人学 · 计算机科学 2025-06-23 Melih Özcan , Ozgur S. Oguz

Most object manipulation strategies for robots are based on the assumption that the object is rigid (i.e., with fixed geometry) and the goal's details have been fully specified (e.g., the exact target pose). However, there are many tasks…

机器人学 · 计算机科学 2022-09-14 Shengzeng Huo , Fangyuan Wang , Luyin Hu , Peng Zhou , Jihong Zhu , Hesheng Wang , David Navarro-Alarcon
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