Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the inference time grows exponentially as the dimension of action space increases. We propose …