Uncertainty Estimation in Vision-based Deep Learning for robotic manipulation sceneries

  • Typ:Masterarbeit
  • Datum:ab sofort
  • Betreuung:

    Yitian Shi



Uncertainty estimation plays an important role in enhancing the safety and effectiveness of critical applications like autonomous driving and robotics. In the realm of robotic manipulation, the policy governing how a robot interacts with its environment and balances the exploitation-exploration dilemma hinges on this concept. A robot may adopt a conservative approach when dealing with potentially hazardous situations, ensuring safety by avoiding risks. Conversely, when encountering novel or unfamiliar scenarios, a robot can turn to an explorative mode. This flexible change is facilitated by uncertainty estimates, which provide the robot with an understanding of unknown factors.




Bayesian uncertainty estimation can be categorized into two sub-areas, namely the Bayesian inference and Ensemble, which involves a full variety of approaches such as variational inference, evidential learning, randomized prior functions, Laplace approximations and sampling-based methods such as Hamiltonian MC, etc. However, most of these paradigms are not tailored for the vision-based deep learning that involves massive state-action space, where a huge research gap exists. For these reasons, our group is now focusing on the transfer of the uncertainty estimation methods into the robot bin-picking sceneries. The main idea is a robust deep learning system that can be aware of out-ofdistribution knowledge utilizing uncertainty and carry out certain calibrations on the predictions as a self-adaptive system.




Solid knowledge base and experience in computer vision, deep learning.

Basic knowledge in reinforcement learning, good experiences also highly expected.

Coding skills in Python and Linux.