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Combining Constrained Sampling and Reinforcement Learning for Manipulation
Marc Toussaint, Cornelius V. Braun, Armand Jordana +5
Training manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demonstrated its strength in such settings, it may st…
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…
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…
RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning
Cornelius V. Braun, Joaquim Ortiz-Haro, Marc Toussaint +1
Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many obje…