activity
20192023
most citedSelf-Paced Contextual Reinforcement Learning

9 citations · 24 across the 8 of their papers we have counts for

collaborators

11 papers

cs.LG20223 cited

How Crucial is Transformer in Decision Transformer?

Max Siebenborn, Boris Belousov, Junning Huang +1

Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem and…

cs.RO2022

Active Exploration for Robotic Manipulation

Tim Schneider, Boris Belousov, Georgia Chalvatzaki +3

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation i…

cs.RO2021

Continuous-Time Fitted Value Iteration for Robust Policies

Michael Lutter, Boris Belousov, Shie Mannor +3

Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential eq…

cs.LG2021

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Pascal Klink, Hany Abdulsamad, Boris Belousov +3

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement…

cs.RO2020

Underactuated Waypoint Trajectory Optimization for Light Painting Photography

Christian Eilers, Jonas Eschmann, Robin Menzenbach +3

Despite their abundance in robotics and nature, underactuated systems remain a challenge for control engineering. Trajectory optimization provides a generally applicable solution,…

cs.LG20194 cited

HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints

Michael Lutter, Boris Belousov, Kim Listmann +2

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learni…