Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

2 code implementations • ICML 2020 • Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik

Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal).

Multi-Objective Reinforcement Learning reinforcement-learning +1

Simplicits: Mesh-Free, Geometry-Agnostic, Elastic Simulation

no code implementations • 9 Jun 2024 • Vismay Modi, Nicholas Sharp, Or Perel, Shinjiro Sueda, David I. W. Levin

The proliferation of 3D representations, from explicit meshes to implicit neural fields and more, motivates the need for simulators agnostic to representation.

ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

no code implementations • 29 Sep 2023 • Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta, Wojciech Matusik

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.

An End-to-End Differentiable Framework for Contact-Aware Robot Design

1 code implementation • 15 Jul 2021 • Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal

Existing methods for co-optimization are limited and fail to explore a rich space of designs.