activity
20182023
most citedCooperative Task and Motion Planning for Multi-Arm Assembly Systems

20 citations · 48 across the 21 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.RO2021

HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

Xin Huang, Guy Rosman, Igor Gilitschenski +4

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicti…

cs.RO2021

Real-Time Risk-Bounded Tube-Based Trajectory Safety Verification

Ashkan Jasour, Weiqiao Han, Brian Williams

In this paper, we address the real-time risk-bounded safety verification problem of continuous-time state trajectories of autonomous systems in the presence of uncertain time-varyi…

cs.LG20211 cited

Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures

Ashkan Jasour, Xin Huang, Allen Wang +1

This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents' futures are generated…

cs.LG2021

Risk Conditioned Neural Motion Planning

Xin Huang, Meng Feng, Ashkan Jasour +2

Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. While existing mathematical programming methods offer theoretical guarantees in the co…

cs.RO2021

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

Ashkan Jasour, Weiqiao Han, Brian Williams

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geome…

cs.AI2021

Generalized Conflict-directed Search for Optimal Ordering Problems

Jingkai Chen, Yuening Zhang, Cheng Fang +1

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively deco…