Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background.
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances.
In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method.
Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment.
Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations.
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations.
PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task.
Robotics Sound Audio and Speech Processing
In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks.
The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task.