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Found 9 papers, 5 papers with code
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
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
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
In this paper, we observe that in such scenarios optimal solution typically lies on the boundary between feasible and infeasible regions of the design space, making it considerably more difficult than that with interior optima.
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
AutoOED: Automated Optimal Experimental Design Platform with Data- and Time-Efficient Multi-Objective Optimization
We present AutoOED, an Automated Optimal Experimental Design platform powered by machine learning to accelerate discovering solutions with optimal objective trade-offs.
AutoOED: Automated Optimal Experiment Design Platform
We present AutoOED, an Optimal Experiment Design platform powered with automated machine learning to accelerate the discovery of optimal solutions.
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
