5 accepted papers
Accurate uncertainty estimation is essential for reliable appearance-based gaze tracking. However, domain shifts between training and testing often lead to incorrect uncertainty estimates, which is a problem overlooked in existing uncertainty-aware gaze tracking models. To overcome this problem effi
Deep Reinforcement Learning (DRL) has shown its capability to solve the high degrees of freedom in control and the complex interaction with the object in the multi-finger dexterous in-hand manipulation tasks. Current DRL approaches lack behavior constraints during the learning process, leading to ag
Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Asymmetric Actor-Critic approaches are used for Sim2Real to reduce the rich idealized features in simulation to the accessible o…
In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning-based approaches mainly focus on training a single robot-structure-specific policy thro…
In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world environments, a human usually only has a general goal (e.g.,