Alberto Silvio Chiappa

This paper develops a closed-loop muscle-level model of primate grasping using imitation learning and neural recordings, revealing muscle-centric and latent-goal codes in sensorimotor cortex.

This paper uses curriculum-based reinforcement learning to train a neural network to control a musculoskeletal hand model, revealing insights into motor control and synergy analysis.

The paper uses task-driven neural networks to predict neural activity in proprioceptive pathways during reaching tasks, finding that task-optimized models outperform other models.