The human fascination to mimic ultra-efficient living beings like insects and birds has led to the rise of small autonomous robots. Smaller robots are safer, more agile and are task distributable as swarms. One might wonder, why do we not have small robots deployed in the wild today? Smaller robots are constrained by a severe dearth of computation and sensor quality. To further exacerbate the situation, today's mainstream approach for autonomy on small robots relies on building a 3D map of the scene that is used to plan paths for executing a control algorithm. Such a methodology has severely bounded the potential of small autonomous robots due to the strict distinction between perception, planning, and control. Instead, we re-imagine each agent by drawing inspiration from insects at the bottom of the size and computation spectrum. Specifically, each of our agents comprises a series of hierarchical competencies built on bio-inspired sensorimotor AI loops by utilizing the action-perception synergy. Here, the agent controls its movement and physical interaction to make up for its lack of computation and sensing. Such an approach imposes additional constraints on the data gathered to solve the problem using Active and Interactive Perception. To unify the class of motion problems, we present a method to exploit the unknown, i.e., the uncertainty of the predictions to obtain additional informational cues in a new theory called Novel Perception that utilizes the statistics of motion fields to tackle various classes of problems from navigation and interaction. This method has the potential to be the go-to mathematical formulation for tackling the class of motion-field-based problems in robotics and made it into the cover of Science Robotics journal.
Co-sponsored by: Media Partner: Open Research Institute (ORI)
Speaker(s): Nitin J. Sanket
Agenda:
– Invited talk from Prof.(https://www.wpi.edu/people/faculty/nitin), from Robotics Engineering department at Worcester Polytechnic Institute.
– Q/A Session
Virtual: https://events.vtools.ieee.org/m/433949
From Mystery to Mastery: Tiny Robot Autonomy using the Manifestation of the Unknown
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