9 papers
STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images
Abhiroop Ajith, Constantinos Chamzas
Robots performing long-horizon visual manipulation observe high-dimensional images, but successful plans depend on action-relevant facts: what can be done now and what changes afte…
AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
Seyedali Golestaneh, Zhuoyun Zhong, Donghyung Lee +1
Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or non-holonomic systems. Howeve…
Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
In many real-world robotic tasks, robots must generate dynamically feasible motions that reliably reach desired goals even under uncertainty. Yet existing sampling-based kinodynami…
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where…
COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
Niranjan Kumar Ilampooranan, Constantinos Chamzas
The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where mo…
Learning Discrete Abstractions for Visual Rearrangement Tasks Using Vision-Guided Graph Coloring
Abhiroop Ajith, Constantinos Chamzas
Learning abstractions directly from data is a core challenge in robotics. Humans naturally operate at an abstract level, reasoning over high-level subgoals while delegating executi…