2,815 research outputs found
Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain
Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors
A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems
Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests
Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common
skill introduces a lot of challenges for robots to achieve: The robots need to
operate such dynamic actions at high-speed, collaborate precisely, and interact
with diverse objects. In this paper, we design a system with two multi-finger
hands attached to robot arms to solve this problem. We train our system using
Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer
to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple
novel algorithm designs including learning a trajectory prediction model for
the object. Such a model can help the robot catcher has a real-time estimation
of where the object will be heading, and then react accordingly. We conduct our
experiments with multiple objects in the real-world system, and show
significant improvements over multiple baselines. Our project page is available
at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023.
https://binghao-huang.github.io/dynamic_handover
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