Neuro Inspired Robotics: What Robot Learning Can Borrow from the Sensorimotor Brain
Neuro Inspired Robotics: What Robot Learning Can Borrow from the Sensorimotor Brain
This paper argues that current robot policies fail at everyday tasks due to design limitations rather than data scarcity. It proposes a neuro-inspired architecture borrowing compliance control from the body, a parallel fast vision system for motor behavior from the visual system, a predictive recalibration mechanism from the cerebellum, and a compositional action model from the motor cortex. These designs aim to enable robots that act on their own initiative, serving as hypotheses for future deployment and recursive self-improvement.
This survey proposes neuro-inspired architectural designs for robot learning, drawing analogies from biological sensorimotor systems to address gaps in everyday manipulation performance.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.02510v1 Announce Type: new Abstract: Robot policies still fail at everyday manipulation: an unfamiliar task, an object never held before, a scene that changes mid-motion, a skill that should improve with use. They fail despite pretraining on over a million trajectories, pooled from dozens of robots at great expense, while a child acquires all of these abilities within a few years of learning from a single
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Source：arXiv Robotics — research abstracts · arxiv.org