7 citations · 33 across the 47 of their papers we have counts for
11 papers · 1 filter
AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations
Zixing Wang, Ahmed H. Qureshi
Anytime 3D human pose forecasting is crucial to synchronous real-world human-machine interaction, where the term ``anytime" corresponds to predicting human pose at any real-valued…
DeRi-IGP: Learning to Manipulate Rigid Objects Using Deformable Objects via Iterative Grasp-Pull
Zixing Wang, Ahmed H. Qureshi
Robotic manipulation of rigid objects via deformable linear objects (DLO) such as ropes is an emerging field of research with applications in various rigid object transportation ta…
Efficient Q-Learning over Visit Frequency Maps for Multi-agent Exploration of Unknown Environments
Xuyang Chen, Ashvin N. Iyer, Zixing Wang +1
The robot exploration task has been widely studied with applications spanning from novel environment mapping to item delivery. For some time-critical tasks, such as rescue catastro…
SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving
Vidyaa Krishnan Nivash, Ahmed H. Qureshi
Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on t…
MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
Vivek Gupta, Praphpreet Dhir, Jeegn Dani +1
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While exist…
Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers
Jacob J Johnson, Ahmed H Qureshi, Michael Yip
Motion planning is integral to robotics applications such as autonomous driving, surgical robots, and industrial manipulators. Existing planning methods lack scalability to higher-…
Progressive Learning for Physics-informed Neural Motion Planning
Ruiqi Ni, Ahmed H. Qureshi
Motion planning (MP) is one of the core robotics problems requiring fast methods for finding a collision-free robot motion path connecting the given start and goal states. Neural m…
Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments
Hanwen Ren, Ahmed H. Qureshi
Non-monotone object rearrangement planning in confined spaces such as cabinets and shelves is a widely occurring but challenging problem in robotics. Both the robot motion and the…
DeRi-Bot: Learning to Collaboratively Manipulate Rigid Objects via Deformable Objects
Zixing Wang, Ahmed H. Qureshi
Recent research efforts have yielded significant advancements in manipulating objects under homogeneous settings where the robot is required to either manipulate rigid or deformabl…
Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Daniel Lawson, Ahmed H. Qureshi
Recent work has shown the promise of creating generalist, transformer-based, models for language, vision, and sequential decision-making problems. To create such models, we general…
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Zikang Xiong, Daniel Lawson, Joe Eappen +2
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamic…