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09/15/2023
Sampling-Free Probabilistic Deep State-Space Models
Many real-world dynamical systems can be described as State-Space Models...
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08/12/2023
Value-Distributional Model-Based Reinforcement Learning
Quantifying uncertainty about a policy's long-term performance is import...
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08/03/2023
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
Learning priors on trajectory distributions can help accelerate robot mo...
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05/02/2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Graph neural networks are often used to model interacting dynamical syst...
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03/08/2023
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
We propose a model predictive control approach for autonomous vehicles t...
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03/07/2023
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Model-based reinforcement learning is one approach to increase sample ef...
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03/01/2023
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
Recent methods for imitation learning directly learn a Q-function using ...
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02/25/2023
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Deployment of reinforcement learning algorithms for robotics application...
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02/24/2023
Model-Based Uncertainty in Value Functions
We consider the problem of quantifying uncertainty over expected cumulat...
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01/09/2023
Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators
Parallel-elastic joints can improve the efficiency and strength of robot...
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12/26/2022
Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation
Vision-based tactile sensors have gained extensive attention in the robo...
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12/09/2022
Information-Theoretic Safe Exploration with Gaussian Processes
We consider a sequential decision making task where we are not allowed t...
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12/04/2022
Hierarchical Policy Blending As Optimal Transport
We present hierarchical policy blending as optimal transport (HiPBOT). T...
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11/29/2022
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
PAC-Bayes has recently re-emerged as an effective theory with which one ...
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11/26/2022
How Crucial is Transformer in Decision Transformer?
Decision Transformer (DT) is a recently proposed architecture for Reinfo...
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11/02/2022
Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics
Well-calibrated probabilistic regression models are a crucial learning c...
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10/23/2022
Active Exploration for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite signif...
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10/22/2022
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction
Modeling interaction dynamics to generate robot trajectories that enable...
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10/14/2022
Hierarchical Policy Blending as Inference for Reactive Robot Control
Motion generation in cluttered, dense, and dynamic environments is a cen...
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10/07/2022
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
Monte Carlo methods have become increasingly relevant for control of non...
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10/05/2022
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing
Tactile sensors are promising tools for endowing robots with embodied in...
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09/12/2022
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning
Effective exploration is critical for reinforcement learning agents in e...
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09/08/2022
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Multi-objective optimization problems are ubiquitous in robotics, e.g., ...
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07/04/2022
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling
Dynamic movements are ubiquitous in human motor behavior as they tend to...
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06/01/2022
Active Inference for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite signif...
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04/11/2022
Learning Implicit Priors for Motion Optimization
In this paper, we focus on the problem of integrating Energy-based Model...
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03/28/2022
Revisiting Model-based Value Expansion
Model-based value expansion methods promise to improve the quality of va...
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03/20/2022
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
As robots play an increasingly important role in the industrial, the exp...
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03/09/2022
Dimensionality Reduction and Prioritized Exploration for Policy Search
Black-box policy optimization is a class of reinforcement learning algor...
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03/09/2022
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Autonomous robots should operate in real-world dynamic environments and ...
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03/08/2022
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery
Robot assembly discovery is a challenging problem that lives at the inte...
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03/08/2022
Robot Learning of Mobile Manipulation with Reachability Behavior Priors
Mobile Manipulation (MM) systems are ideal candidates for taking up the ...
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03/08/2022
A Hierarchical Approach to Active Pose Estimation
Creating mobile robots which are able to find and manipulate objects in ...
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03/08/2022
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives
It is desirable for future robots to quickly learn new tasks and adapt l...
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03/08/2022
An Analysis of Measure-Valued Derivatives for Policy Gradients
Reinforcement learning methods for robotics are increasingly successful ...
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03/07/2022
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
We present a PAC-Bayesian analysis of lifelong learning. In the lifelong...
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03/03/2022
An Adaptive Human Driver Model for Realistic Race Car Simulations
Engineering a high-performance race car requires a direct consideration ...
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03/02/2022
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images
Recent methods for reinforcement learning from images use auxiliary task...
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02/11/2022
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex...
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01/24/2022
Learning Geometric Constraints in Task and Motion Planning
Searching for bindings of geometric parameters in task and motion planni...
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12/06/2021
Distilled Domain Randomization
Deep reinforcement learning is an effective tool to learn robot control ...
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11/11/2021
Model-Based Reinforcement Learning for Stochastic Hybrid Systems
Optimal control of general nonlinear systems is a central challenge in a...
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11/01/2021
Robot Learning from Randomized Simulations: A Review
The rise of deep learning has caused a paradigm shift in robotics resear...
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10/24/2021
A Differentiable Newton-Euler Algorithm for Real-World Robotics
Obtaining dynamics models is essential for robotics to achieve accurate ...
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10/22/2021
Learning Stable Vector Fields on Lie Groups
Learning robot motions from demonstration requires having models that ar...
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10/05/2021
Continuous-Time Fitted Value Iteration for Robust Policies
Solving the Hamilton-Jacobi-Bellman equation is important in many domain...
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10/05/2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
Deep learning has been widely used within learning algorithms for roboti...
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07/20/2021
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients
Reinforcement learning methods for robotics are increasingly successful ...
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07/13/2021
Efficient and Reactive Planning for High Speed Robot Air Hockey
Highly dynamic robotic tasks require high-speed and reactive robots. The...
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06/07/2021
