activity
20242026
collaborators

11 papers

cs.RO2026

VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Aniket Datar +1

Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models ofte…

cs.RO2026

Balancing Accuracy and Efficiency: Adaptive Dynamics Orchestration for Model Predictive Control

Francesco Cancelliere, Aniket Datar, Giovanni Muscato +1

Model Predictive Control (MPC) for autonomous navigation faces a fundamental trade-off between model accuracy and real-time efficiency. High-fidelity dynamics models can accurately…

cs.RO2026

Zero-Shot Adaptation to Robot Structural Damage via Natural Language-Informed Kinodynamics Modeling

Anuj Pokhrel, Aniket Datar, Mohammad Nazeri +2

High-performance autonomous mobile robots endure significant mechanical stress during in-the-wild operations, e.g., driving at high speeds or over rugged terrain. Although these pl…

cs.RO2025

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy

Haiyue Chen, Aniket Datar, Tong Xu +6

Off-road navigation is an important capability for mobile robots deployed in environments that are inaccessible or dangerous to humans, such as disaster response or planetary explo…

cs.RO2025

Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Madhan B. Rao +4

Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both…

cs.RO2025

M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions

Aniket Datar, Anuj Pokhrel, Mohammad Nazeri +8

Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy system…