1 citations · 1 across the 6 of their papers we have counts for
6 papers
Search, Ground, Plan: Functional Sufficiency for Task and Motion Planning under Incomplete Scene Knowledge
Narendhiran Vijayakumar, Nav Singhal, Girish Varma +1
Foundation models (FMs) have expanded task and motion planning (TAMP) to manipulation problems specified through language and visual observations. However, incomplete scene knowled…
Deciding If, When, and Where to Replan for Robust Manipulation
Puru Ojha, Narendhiran Vijayakumar, Nav Singhal +2
Foundation-model (FM) based task and motion planning (TAMP) methods typically assume that the full scene is known before planning. In long-horizon manipulation, however, relevant o…
Learning Motion Feasibility from Point Clouds in Cluttered Environments
Sajid Ansari, Arthi, Girish Varma +1
Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environm…
Dual-Signal Adaptive KV-Cache Optimization for Long-Form Video Understanding in Vision-Language Models
Vishnu Sai, Dheeraj Sai, Srinath B +2
Vision-Language Models (VLMs) face a critical memory bottleneck when processing long-form video content due to the linear growth of the Key-Value (KV) cache with sequence length. E…
ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions
Furqan Ahmed Shaik, Sandeep Nagar, Aiswarya Maturi +7
The ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions served as a rigorous platform to evaluate and benchmark state-…
IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather
Furqan Ahmed Shaik, Abhishek Malreddy, Nikhil Reddy Billa +3
Large-scale deployment of fully autonomous vehicles requires a very high degree of robustness to unstructured traffic, and weather conditions, and should prevent unsafe mispredicti…