Embodied robotic manipulation investigates robotic and prosthetic limbs that physically interact with the environment using human-like, adaptive control. Work in this area integrates soft robotic structures, tendon-driven and biohybrid actuators, and exoskeletons with rich multimodal sensing, including vision, tactile, and proprioceptive feedback. Machine learning methods such as imitation learning, reinforcement learning, and meta-learning are used to acquire dexterous skills, enable shared and autonomous control, and support intuitive human–robot interaction through haptic interfaces, brain–computer interfaces, and teleoperation systems.
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