The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

This work aims at reviewing, analyzing and comparing a range of state-of-the-art
approaches to inertial parameter identification in the context of robotics. We introduce …

Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …

Sampling-based model-predictive control (MPC) is a promising tool for feedback control of
robots with complex, non-smooth dynamics, and cost functions. However, the …

We formulate grasp learning as a neural field and present Neural Grasp Distance Fields
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …

Existing grasp synthesis methods are either analytical or data-driven. The former one is
oftentimes limited to specific application scope. The latter one depends heavily on …

Deep learning has been widely used within learning algorithms for robotics. One
disadvantage of deep networks is that these networks are black-box representations …

Combining domain randomization and reinforcement learning is a widely used approach to
obtain control policies that can bridge the gap between simulation and reality. However …

A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the
learned models. Blackbox models can fit complex dynamics with high fidelity, but their …