1 citations · 2 across the 5 of their papers we have counts for
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TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping
Manav Kulshrestha, Apoorva Vashisth, Damon Conover +1
Task-Oriented Grasping (TOG) has emerged as an important skill for purposeful robotic manipulation. Given the task of using a knife to "slice bread," a robot must choose to grasp f…
CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation
Apoorva Vashisth, Manav Kulshrestha, Pranav Bakshi +3
Robot navigation typically assumes an obstacle-free path exists between start and goal. In real environments, however, clutter may block all routes. We introduce Lifelong Interacti…
VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models
Manav Kulshrestha, S. Talha Bukhari, Damon Conover +1
Robotic grasping is a fundamental capability for enabling autonomous manipulation, with usually infinite solutions. State-of-the-art approaches for grasping rely on learning from l…
Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
Apoorva Vashisth, Manav Kulshrestha, Damon Conover +1
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms o…
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Manav Kulshrestha, Ahmed H. Qureshi
Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primaril…