Abstract:  Designing safe and reliable robotic assistance for caregiving is a grand challenge in robotics. A sixth of the United States population is over the age of 65 and more than 1 in 4 (over 70 million) adults in the United States reported having a disability in 2022. Robotic caregivers could positively benefit society; yet, physical robotic assistance presents several challenges and open research questions relating to autonomous control, multimodal sensing and learning, and accessible interfaces. In this talk, I will present recent techniques and technology that my group has developed towards addressing core challenges in robotic caregiving. First, I will introduce inertial and high-density electromyography (HDEMG) wearable interfaces that enable people with severe loss of motor and hand function (due to spinal cord injury or neurodegenerative diseases) to embody physically assistive mobile manipulators in their home. I will then present our recent work in robot learning, including online and offline policy learning, to perform complex manipulation in assistive scenarios. This includes learning reward functions and robot control policies, sim-to-real transfer, a new technique to scale imitation learning for bimanual manipulation, and new opportunities presented by generative simulation.

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