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Understanding Shared Control for Assistive Robotic Arms
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Living a self-determined life independent of human caregivers or fully autonomous robots is a crucial factor for human dignity and the preservation of self-worth for people with motor impairments. Assistive robotic solutions - particularly robotic arms - are frequently deployed in domestic care, empowering people with motor impairments in performing ADLs independently. However, while assistive robotic arms can help them perform ADLs, currently available controls are highly complex and time-consuming due to the need to control multiple DoFs at once and necessary mode-switches. This work provides an overview of shared control approaches for assistive robotic arms, which aim to improve their ease of use for people with motor impairments. We identify three main takeaways for future research: Less is More, Pick-and-Place Matters, and Communicating Intent.
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Cited by 1 Pith paper
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Shared Control of Holonomic Wheelchairs through Reinforcement Learning
An RL policy trained in Isaac Gym and tested in Gazebo and on a real DAA V1 wheelchair translates 2D joystick commands into collision-free 3D motion for a holonomic wheelchair.
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