10 accepted papers
Robot manipulation relying on learned object-centric descriptors became popular in recent years. Visual descriptors can easily describe manipulation task objectives, they can be learned efficiently using self-supervision, and they can encode actuated and even non-rigid objects. However, learning rob…
Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consid…
We propose a framework for robust and efficient training of Dense Object Nets (DON) [1] with a focus on industrial multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot m…
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex datasets, such as registered RGBD sequences, we train on an unordered set of RGB images. This allows for learning from a s…
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object in…
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several ski…
When navigating mobile robotic systems in dynamic environments, the ability to predict where pedestrians will move in the next few seconds is crucial. To tackle this problem, many solutions have been developed which take the environment's influence on human navigation behavior into account. However,
The problem of minimizing cost in nonlinear control systems with uncertainties or disturbances remains a major challenge. Model predictive control (MPC), and in particular sampling-based MPC has recently shown great success in complex domains such as aggressive driving with highly nonlinear dynamics…
Guaranteeing safe constraint satisfaction in nonlinear control systems with uncertainty remains a major challenge for control. The most successful control method handling constraints under uncertainty has without doubt been model predictive control (MPC). In particular, recent sampling-based MPC met…
As robots are increasingly entering human environments, many subtleties of socially compliant navigation are still unsolved. To behave in a socially compliant way, robots need to have an understanding of the natural motion paths of humans in the shared environment. Humans intuitively follow social n