8 citations · 9 across the 14 of their papers we have counts for
15 papers
AERMANI-PLACE: Language Guided Object Placement with Aerial Manipulators
Sarthak Mishra, Ritama Sanyal, Rishabh Dev Yadav +2
Object placement is a fundamental component of aerial manipulation tasks, yet existing systems typically require the desired placement position to be specified explicitly in metric…
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
Rishabh Dev Yadav, Samaksh Ujjawal, Shivansh Pratap Singh +4
Accurate dynamics modeling of aerial manipulators is challenging because platform--manipulator coupling, history-dependent interactions, and changing payload or manipulator configu…
Composer 2 Technical Report
Cursor Research, :, Aaron Chan +53
Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability…
AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments
Shivansh Pratap Singh, Naveen Sudheer Nair, Samaksh Ujjawal +4
Reliable aerial grasping in cluttered environments remains challenging due to occlusions and collision risks. Existing aerial manipulation pipelines largely rely on centroid-based…
Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators
Samaksh Ujjawal, Naveen Sudheer Nair, Shivansh Pratap Singh +3
Autonomous Aerial Manipulators (AAMs) are inherently coupled, nonlinear systems that exhibit nonstationary and multiscale residual dynamics, particularly during manipulator reconfi…
AeroPlace-Flow: Language-Grounded Object Placement for Aerial Manipulators via Visual Foresight and Object Flow
Sarthak Mishra, Rishabh Dev Yadav, Naveen Nair +2
Precise object placement remains underexplored in aerial manipulation, where most systems rely on predefined target coordinates and focus primarily on grasping and control. Specify…
ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning
Rishabh Dev Yadav, Avirup Das, Hongyu Song +2
Real-world robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, tr…
AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators
Samaksh Ujjawal, Shivansh Pratap Singh, Naveen Sudheer Nair +3
Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliab…
AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models
Sarthak Mishra, Rishabh Dev Yadav, Avirup Das +3
The rapid progress of vision--language models (VLMs) has sparked growing interest in robotic control, where natural language can express the operation goals while visual feedback l…
SoraNav: Adaptive UAV Task-Centric Navigation via Zeroshot VLM Reasoning
Hongyu Song, Rishabh Dev Yadav, Cheng Guo +1
Autonomous navigation under natural language instructions represents a crucial step toward embodied intelligence, enabling complex task execution in environments ranging from indus…
Impedance and Stability Targeted Adaptation for Aerial Manipulator with Unknown Coupling Dynamics
Amitabh Sharma, Saksham Gupta, Shivansh Pratap Singh +5
Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved v…
Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces
Rishabh Dev Yadav, Swati Dantu, Wei Pan +3
Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this…
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
Avirup Das, Rishabh Dev Yadav, Sihao Sun +3
An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, mak…
Computation-Aware Learning for Stable Control with Gaussian Process
Wenhan Cao, Alexandre Capone, Rishabh Yadav +2
In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end pro…
Seeing Through Uncertainty: Free-Energy-Inspired Real-Time Adaptation for Robust Visual Navigation
Maytus Piriyajitakonkij, Rishabh Dev Yadav, Mingfei Sun +2
Navigation in the natural world is a feat of adaptive inference, where biological organisms maintain goal-directed behaviour despite noisy and incomplete sensory streams. Central t…