A latent control model for realistic rodent motion

Key points

• The development of embodied models of animal behavior is a major goal of the neuro-AI community.1
• We show that combining both of these priors allows SCAMPER to generate realistic behavior during exploration and exploit the task structure effectively.1
• SCAMPER can also learn from sparse rewards as a result of the diverse movements induced by the behavior prior, allowing the system to model realistic sparse-reward tasks from experimental neuroscience.1
• Altogether, SCAMPER provides the neuro-AI community with an effective new system for training embodied models of rodents on realistic tasks.1

Sources (1)

• [1]A latent control model for realistic rodent motion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:54 PM

  Here, we introduce Simulated Control via Augmented Motor Priors for Embodied Reinforcement learning (SCAMPER), a system for training an embodied virtual rat model, enabling natural movements during both goal-directed behavior and random exploration.

  The development of embodied models of animal behavior is a major goal of the neuro-AI community.

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