5 papers
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…
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…
WorldPlanner: Monte Carlo Tree Search and MPC with Action-Conditioned Visual World Models
R. Khorrambakht, Joaquim Ortiz-Haro, Joseph Amigo +4
Robots must understand their environment from raw sensory inputs and reason about the consequences of their actions in it to solve complex tasks. Behavior Cloning (BC) leverages ta…
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…
Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions
Shiqing Wei, Rooholla Khorrambakht, Prashanth Krishnamurthy +2
Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method…