47 citations · 48 across the 7 of their papers we have counts for
8 papers · 1 filter
FLEX-WAM: Flexible Block-Causal World-Action Models for Long-Horizon Imagination and Planning
R. Khorrambakht, Joseph Amigo, Félix Lebel +4
World--action models (WAMs) promise a unified model that predicts action-conditioned futures, generates feasible actions, and supports planning in imagination. However, existing jo…
CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism
R. Khorrambakht, Joaquim Ortiz-Haro, Stephan Weiss +1
Incorporating dense visual information into motion planning remains challenging, as geometric planners rely on abstracted scene representations that discard visual richness, while…
Coupled Local and Global World Models for Efficient First Order RL
Joseph Amigo, Rooholla Khorrambakht, Nicolas Mansard +1
World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex sensory information, such as visual pe…
First Order Model-Based RL through Decoupled Backpropagation
Joseph Amigo, Rooholla Khorrambakht, Elliot Chane-Sane +2
There is growing interest in reinforcement learning (RL) methods that leverage the simulator's derivatives to improve learning efficiency. While early gradient-based approaches hav…
Open-Architecture End-to-End System for Real-World Autonomous Robot Navigation
Venkata Naren Devarakonda, Ali Umut Kaypak, Raktim Gautam Goswami +4
Enabling robots to autonomously navigate unknown, complex, and dynamic real-world environments presents several challenges, including imperfect perception, partial observability, l…
Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions
Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy +1
The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based a…
Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance
Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy +1
Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based metho…
Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions
Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy +3
Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remain…