2 citations · 4 across the 7 of their papers we have counts for
9 papers
CLARO: Clutter Aware Robot Orchestrator for Open-Vocabulary Grasping using VLM Agents
Sungsu Kim, Vineet Bhat, Prashanth Krishnamurthy +2
Robots that follow natural-language instructions unlock a wide range of autonomous applications in households, warehouses, and labs. Real-world environments are diverse and often h…
MG-VQA: Manipulation Grounded Visual Question Answering with VLMs
Vineet Bhat, Siyi Chen, Mikaela Angelina Uy +6
Vision-language models (VLMs) have shown promising spatial reasoning capabilities from static visual inputs, where the evidence needed to answer a question is available in the prov…
RESCORE: LLM-Driven Simulation Recovery in Control Systems Research Papers
Vineet Bhat, Shiqing Wei, Ali Umut Kaypak +3
Reconstructing numerical simulations from control systems research papers is often hindered by underspecified parameters and ambiguous implementation details. We define the task of…
BOP-ASK: Object-Interaction Reasoning for Vision-Language Models
Vineet Bhat, Sungsu Kim, Valts Blukis +6
Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interacti…
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping
Vineet Bhat, Naman Patel, Prashanth Krishnamurthy +2
Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable grasp poses from natural language…
3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks
Vineet Bhat, Yu-Hsiang Lan, Prashanth Krishnamurthy +2
Robotic manipulation in 3D requires effective computation of N degree-of-freedom joint-space trajectories that enable precise and robust control. To achieve this, robots must integ…
Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI
Jitendra Bhandari, Vineet Bhat, Yuheng He +3
Masala-CHAI is a fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a…
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models
Vineet Bhat, Prashanth Krishnamurthy, Ramesh Karri +1
Robots interacting with humans through natural language can unlock numerous applications such as Referring Grasp Synthesis (RGS). Given a text query, RGS determines a stable grasp…
Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy +2
Planning algorithms decompose complex problems into intermediate steps that can be sequentially executed by robots to complete tasks. Recent works have employed Large Language Mode…