activity
20192026
most citedA gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

4 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

ContactIPM: A Structure-Exploiting Interior-Point Solver for Contact-Implicit Trajectory Optimization

Yucheng Chen

Contact-implicit trajectory optimization avoids prescribing contact sequences, but yields mathematical programs with complementarity constraints (MPCCs) whose degeneracy challenges…

cs.RO2026

Feasibility-Guided Planning over Multi-Specialized Locomotion Policies

Ying-Sheng Luo, Lu-Ching Wang, Hanjaya Mandala +6

Planning over unstructured terrain presents a significant challenge in the field of legged robotics. Although recent works in reinforcement learning have yielded various locomotion…

cs.RO2026

TurboADMM: A Structure-Exploiting Parallel Solver for Multi-Agent Trajectory Optimization

Yucheng Chen

Multi-agent trajectory optimization with dense interaction networks require solving large coupled QPs at control rates, yet existing solvers fail to simultaneously exploit temporal…

cs.RO2023

Heuristic Satisficing Inferential Decision Making in Human and Robot Active Perception

Yucheng Chen, Pingping Zhu, Anthony Alers +3

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore,…

cs.LG2019★ 4 cited

A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

Yucheng Chen, Matus Telgarsky, Chao Zhang +3

This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The app…