activity
20202022
most citedSafe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction

17 citations · 29 across the 4 of their papers we have counts for

collaborators

8 papers

eess.SY202217 cited

Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction

Jesse Jiang, Ye Zhao, Samuel Coogan

We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Lo…

cs.RO202110 cited

Terrain-perception-free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation

Hongwu Zhu, Dong Wang, Nathan Boyd +6

Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to trav…

cs.RO2021

Mediating between Contact Feasibility and Robustness of Trajectory Optimization through Chance Complementarity Constraints

Luke Drnach, John Z. Zhang, Ye Zhao

As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, plann…

cs.RO2020

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

Zhigen Zhao, Ziyi Zhou, Michael Park +1

This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequent…

cs.RO20202 cited

Robust Trajectory Optimization over Uncertain Terrain with Stochastic Complementarity

Luke Drnach, Ye Zhao

Trajectory optimization with contact-rich behaviors has recently gained attention for generating diverse locomotion behaviors without pre-specified ground contact sequences. Howeve…

cs.RO2020

Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain

Jonas Warnke, Abdulaziz Shamsah, Yingke Li +1

This study proposes an integrated task and motion planning method for dynamic locomotion in partially observable environments with multi-level safety guarantees. This layered plann…