5 papers
Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control
Denis Shcherba, Adrian Abel, Eckart Cobo-Briesewitz +3
Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires acting from partial observatio…
Stability-Guided Exploration for Diverse Motion Generation
Eckart Cobo-Briesewitz, Tilman Burghoff, Denis Shcherba +2
Scaling up datasets is highly effective in improving the performance of deep learning models, including in the field of robot learning. However, data collection still proves to be…
Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation
Marc Toussaint, Cornelius V. Braun, Armand Jordana +5
Training non-prehensile manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demonstrated its strength in such sett…
Meta-Optimization and Program Search using Language Models for Task and Motion Planning
Denis Shcherba, Eckart Cobo-Briesewitz, Cornelius V. Braun +1
Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. Task and motion planning (TAMP) addresses this b…
Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads
Eckart Cobo-Briesewitz, Khaled Wahba, Wolfgang Hönig
The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Conventional controllers model mos…