6 papers
Manifold Sampling via Entropy Maximization
Cornelius V. Braun, Tilman Burghoff, Marc Toussaint
Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes convergence and feasibility guaran…
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…
Trajectory First: A Curriculum for Discovering Diverse Policies
Cornelius V. Braun, Sayantan Auddy, Marc Toussaint
Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima. In this context, constrained diversity optimization has becom…
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…
Stein Variational Evolution Strategies
Cornelius V. Braun, Robert T. Lange, Marc Toussaint
Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the…
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
Marc Toussaint, Cornelius V. Braun, Joaquim Ortiz-Haro
Generating diverse samples under hard constraints is a core challenge in many areas. With this work we aim to provide an integrative view and framework to combine methods from the…