1 citations · 2 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
HEROIC: Heterogeneous Evidential Reasoning for Open-Vocabulary Identification and Cross-Robot Collaboration
Mihir Chauhan, Aarav Jain, Addison Zucek +3
Multi-agent heterogeneous air-ground robot teams are attractive for open world search, with applications for reconnaissance, urban search and rescue missions (USAR), disaster respo…
COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction
Phu Pham, Damon Conover, Aniket Bera
Active 3D reconstruction requires selecting informative viewpoints under limited sensing budgets. In multi-agent settings, coordination inefficiencies such as redundant observation…
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…
HAVEN: Hierarchical Adversary-aware Visibility-Enabled Navigation with Cover Utilization using Deep Transformer Q-Networks
Mihir Chauhan, Damon Conover, Aniket Bera
Autonomous navigation in partially observable environments requires agents to reason beyond immediate sensor input, exploit occlusion, and ensure safety while progressing toward a…
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…
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion
Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang +3
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models a…
Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM
Phu Pham, Dipam Patel, Damon Conover +1
We introduce Go-SLAM, a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments while embedding object-level information within the scene repre…
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…