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Due to the extremely weak intensity of the biomagnetic field and the serious interference from the environmental magnetic field, the detection of the biomagnetic field becomes such challenging work. After analyzing the deficiencies in the current biomagnetic field sensors, this paper proposes and realizes a high-sensitivity magnetic field sensor, based on the giant magneto-impedance (GMI) effect. Taking advantage of the miniaturized magnetic probe, the multistage multiple amplification and the multiband interference suppression, our sensor mainly makes three achievements: the pT level magnetic resolution, the ability to detect the muscle magnetic field without the magnetic shielding and the resistibility to a small-range wobbling in the state of working, which makes it possible to detect the biomagnetic field by wearable sensors under natural conditions.

Reinforcement learning (RL) empowers the agent to learn robotic manipulation skills autonomously. Compared with traditional single-goal RL, semantic-goal-conditioned RL expands the agent capacity to accomplish multiple semantic manipulation instructions. However, due to sparsely distributed semantic goals and sparse-reward agent-environment interactions, the hard exploration problem arises and impedes the agent training process. In traditional RL, curiosity-motivated exploration shows effectiveness in solving the hard exploration problem. However, in semantic-goal-conditioned RL, the performance of previous curiosity-motivated methods deteriorates, which we propose is because of their two defects: uncontrollability and distraction. To solve these defects, we propose a conservative curiosity-motivated method named mutual information motivation with hybrid policy mechanism (MIHM). MIHM mainly contributes two innovations: the decoupled-mutual-information-based intrinsic motivation, which prevents the agent from being motivated to explore dangerous states by uncontrollable curiosity; the precisely trained and automatically switched hybrid policy mechanism, which eliminates the distraction from the curiosity-motivated policy and achieves the optimal utilization of exploration and exploitation. Compared with four state-of-the-art curiosity-motivated methods in the sparse-reward robotic manipulation task with 35 valid semantic goals, including stacks of 2 or 3 objects and pyramids, our MIHM shows the fastest learning speed. Moreover, MIHM achieves the highest 0.9 total success rate, which is up to 0.6 in other methods. Throughout all the baseline methods, our MIHM is the only one that achieves to stack three objects.

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