9 accepted papers
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models trained on SPC control sequences collected in simulation. Unlike prior work relying on iterative refinement or gradient-ba
Sampling-based model predictive control (MPC) is experiencing a resurgence in robotics following both recent hardware successes and advancements in parallelized physics simulation. However, to build on this progress, the robotics community needs to develop shared tools for prototyping, benchmarking,…
The ability to flexibly leverage limbs for loco-manipulation is essential for enabling autonomous robots to operate in unstructured environments. Yet, prior work on loco-manipulation is often constrained to specific tasks or predetermined limb configurations. In this work, we present einforcement Le…
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specif…
We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models. From the density field, we estimate the dynamical properties
We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Classical physics engines rely on non-differentiable collision detection modules. More recent optimization-based approaches simulating contact between conve
We present a framework for bi-level trajectory optimization in which a system’s dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representati
Existing game-theoretic planning methods assume that the robot knows the objective functions of the other agents a priori while, in practical scenarios, this is rarely the case. This letter introduces LUCIDGames, an inverse optimal control algorithm that is able to estimate the other agents' objecti
Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces ALGAMES (Augmented Lagrangian GAME-theoretic Solver), a solver that handles trajectory optimization problems with multiple actors and general nonlinear state and input constrain…