Robots can be powerful tools to advance basic scientific discovery.
Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning-based routing (autonomy module) and synthetic aperture radar-based very high frequency (VHF) signal-based bearing estimation (sensing module) for maximizing rendezvous opportunities of autonomous robots with sperm whales. The sensing module is compatible with low-energy VHF tags commonly used for tracking wildlife. The autonomy module leverages in situ noisy bearing measurements of whale vocalizations, VHF tags, and whale dive behaviors to enable time-critical rendezvous of a robot team with multiple whales in simulation. We conducted experiments at sea in the native habitat of sperm whales using an "engineered whale"-a speedboat equipped with a VHF-emitting tag, emulating five distinct whale tracks, with different whale motions. The sensing module shows a median bearing error of 10.55° to the tag. Using bearing measurements to the engineered whale from an acoustic sensor and our sensing module, our autonomy module gives an aggregate rendezvous success rate of 81.31% for a 500-meter rendezvous distance using three robots in postprocessing. A second class of fielded experiments that used acoustic-only bearing measurements to three untagged sperm whales showed an aggregate rendezvous success rate of 68.68% for a 1000-meter rendezvous distance using two robots in postprocessing. We further validated these algorithms with several ablation studies using a sperm whale visual encounter dataset collected by marine biologists.
Robotics can play a useful role in the scientific understanding of the sense of self, both through the construction of embodied models of the self and through the use of robots as experimental probes to explore the human self. In both cases, the embodiment of the robot allows us to devise and test hypotheses about the nature of the self, with regard to its development, its manifestation in behavior, and the diversity of selves in humans, animals, and, potentially, machines. This paper reviews robotics research that addresses the topic of the self-the minimal self, the extended self, and disorders of the self-and highlights future directions and open challenges in understanding the self through constructing its components in artificial systems. An emerging view is that key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.
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