2 citations · 4 across the 5 of their papers we have counts for
6 papers
NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception
Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang +9
Differentiable simulators have advanced policy learning and model-based control across robotic tasks. Yet actuator dynamics remain underexplored and can be a major source of sim-to…
ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui +6
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its perf…
SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui +5
Optimizing an experimental system can be extremely challenging when each experiment is expensive, time-consuming, or difficult to perform. Existing optimizers for expensive black-b…
Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning
Yunsheng Tian, Joshua Jacob, Yijiang Huang +10
Multi-part assembly poses significant challenges for robots to execute long-horizon, contact-rich manipulation with generalization across complex geometries. We present Fabrica, a…
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
Yunsheng Tian, Ane Zuniga, Xinwei Zhang +5
Bayesian optimization has been successfully applied to optimize black-box functions where the number of evaluations is severely limited. However, in many real-world applications, i…
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari +10
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper…