Many real-world dynamical systems can be described as State-Space Models...
Quantifying uncertainty about a policy's long-term performance is import...
Learning priors on trajectory distributions can help accelerate robot mo...
Graph neural networks are often used to model interacting dynamical syst...
We propose a model predictive control approach for autonomous vehicles t...
Model-based reinforcement learning is one approach to increase sample
ef...
Recent methods for imitation learning directly learn a Q-function using ...
Deployment of reinforcement learning algorithms for robotics application...
We consider the problem of quantifying uncertainty over expected cumulat...
Parallel-elastic joints can improve the efficiency and strength of robot...
Vision-based tactile sensors have gained extensive attention in the robo...
We consider a sequential decision making task where we are not allowed t...
We present hierarchical policy blending as optimal transport (HiPBOT). T...
PAC-Bayes has recently re-emerged as an effective theory with which one ...
Decision Transformer (DT) is a recently proposed architecture for
Reinfo...
Well-calibrated probabilistic regression models are a crucial learning
c...
Robotic manipulation stands as a largely unsolved problem despite signif...
Modeling interaction dynamics to generate robot trajectories that enable...
Motion generation in cluttered, dense, and dynamic environments is a cen...
Monte Carlo methods have become increasingly relevant for control of
non...
Tactile sensors are promising tools for endowing robots with embodied
in...
Effective exploration is critical for reinforcement learning agents in
e...
Multi-objective optimization problems are ubiquitous in robotics, e.g., ...
Dynamic movements are ubiquitous in human motor behavior as they tend to...
Robotic manipulation stands as a largely unsolved problem despite signif...
In this paper, we focus on the problem of integrating Energy-based Model...
Model-based value expansion methods promise to improve the quality of va...
As robots play an increasingly important role in the industrial, the
exp...
Black-box policy optimization is a class of reinforcement learning algor...
Autonomous robots should operate in real-world dynamic environments and
...
Robot assembly discovery is a challenging problem that lives at the
inte...
Mobile Manipulation (MM) systems are ideal candidates for taking up the ...
Creating mobile robots which are able to find and manipulate objects in ...
It is desirable for future robots to quickly learn new tasks and adapt
l...
Reinforcement learning methods for robotics are increasingly successful ...
We present a PAC-Bayesian analysis of lifelong learning. In the lifelong...
Engineering a high-performance race car requires a direct consideration ...
Recent methods for reinforcement learning from images use auxiliary task...
Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex...
Searching for bindings of geometric parameters in task and motion planni...
Deep reinforcement learning is an effective tool to learn robot control
...
Optimal control of general nonlinear systems is a central challenge in
a...
The rise of deep learning has caused a paradigm shift in robotics resear...
Obtaining dynamics models is essential for robotics to achieve accurate
...
Learning robot motions from demonstration requires having models that ar...
Solving the Hamilton-Jacobi-Bellman equation is important in many domain...
Deep learning has been widely used within learning algorithms for roboti...
Reinforcement learning methods for robotics are increasingly successful ...
Highly dynamic robotic tasks require high-speed and reactive robots. The...
We introduce a method based on deep metric learning to perform Bayesian
...