Machine learning techniques are playing more and more important roles in finance market
investment. However, finance quantitative modeling with conventional supervised learning …

The study of animal locomotion and neuromechanical control offers valuable insights for
advancing research in neuroscience, biomechanics, and robotics. We have developed …

Tracking the pose of an object while it is being held and manipulated by a robot hand is
difficult for vision-based methods due to significant occlusions. Prior works have explored …

Representing and reasoning about uncertainty is crucial for autonomous agents acting in
partially observable environments with noisy sensors. Partially observable Markov decision …

This paper presents Gym-Ignition, a new framework to create reproducible robotic
environments for reinforcement learning research. It interfaces with the new generation of …

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel,
eg, sliding an object to a goal pose while maintaining contact with a table. Individual …

With the development of deep learning, fingerprints recognition based on neural networks is
a widely used method in indoor localization. In this paper, we build a long short-term …

Robots need to manipulate objects in constrained environments like shelves and cabinets
when assisting humans in everyday settings like homes and offices. These constraints make …

Active sensing and planning in unknown, cluttered environments is an open challenge for
robots intending to provide home service, search and rescue, narrow-passage inspection …