100 citations · 186 across the 13 of their papers we have counts for
14 papers
Learning Feasibility of Factored Nonlinear Programs in Robotic Manipulation Planning
Joaquim Ortiz-Haro, Jung-Su Ha, Danny Driess +2
A factored Nonlinear Program (Factored-NLP) explicitly models the dependencies between a set of continuous variables and nonlinear constraints, providing an expressive formulation…
Reinforcement Learning with Neural Radiance Fields
Danny Driess, Ingmar Schubert, Pete Florence +2
It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents. This paper demonstrates that learning state representations with su…
FC: Feasibility-Based Control Chain Coordination
Jason Harris, Danny Driess, Marc Toussaint
Hierarchical coordination of controllers often uses symbolic state representations that fully abstract their underlying low-level controllers, treating them as "black boxes" to the…
Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation
Marc Toussaint, Jason Harris, Jung-Su Ha +2
Task and Motion Planning has made great progress in solving hard sequential manipulation problems. However, a gap between such planning formulations and control methods for reactiv…
Learning Multi-Object Dynamics with Compositional Neural Radiance Fields
Danny Driess, Zhiao Huang, Yunzhu Li +2
We present a method to learn compositional multi-object dynamics models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural…
Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input
Jung-Su Ha, Danny Driess, Marc Toussaint
Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or mo…