Robotics Posts - Page 2

We propose a hierarchical planning algorithm in learned latent spaces. Our method uses deep generative models to prioritize promising actions for sampling-based planning.

To create human-like robots, we need to understand how humans behave. We present a modeling approach enables robots to anticipate that humans will make suboptimal choices when risk and uncertainty are involved.

We introduce RoboNet, an open database for sharing robotic experience, and study how this data can be used to learn generalizable models for vision-based robotic manipulation. We find that pre-training on RoboNet enables faster learning in new environments compared to learning from scratch.

We want to make it easier for humans to teleoperate dexterous robots. We present a learning approach that embeds high-dimensional robot actions into an intuitive, human-controllable, and low-dimensional latent space.

We built a system that enables collecting large-scale robot manipulation datasets with human supervision and used it to collect the largest robot dataset ever collected via teleoperation.

When groups robots work together, their actions communicate valuable information. We introduce a collaborative learning and control strategy that enables robots to harness the information contained within their partner's actions.

Presenting AC-Teach, a unifying approach to leverage advice from an ensemble of sub-optimal teachers in order to accelerate the learning process of actor-critic reinforcement learning agents.

Efficient reward learning is hard. With a focus on preference-based learning methods, we show how sample-efficiency can be achieved along with computational efficiency by using batch-active methods.