3 citations · 5 across the 5 of their papers we have counts for
5 papers
SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation
Ruisen Tu, Arth Shukla, Sohyun Yoo +5
Vision-Language-Action (VLA) models show promise for robotic control, yet performance in complex household environments remains sub-optimal. Mobile manipulation requires reasoning…
Seeing the Bigger Picture: 3D Latent Mapping for Mobile Manipulation Policy Learning
Sunghwan Kim, Woojeh Chung, Zhirui Dai +5
In this paper, we demonstrate that mobile manipulation policies utilizing a 3D latent map achieve stronger spatial and temporal reasoning than policies relying solely on images. We…
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
Arth Shukla, Stone Tao, Hao Su
High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as…
ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
Stone Tao, Fanbo Xiang, Arth Shukla +20
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks an…
Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning
Stone Tao, Arth Shukla, Tse-kai Chan +1
Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve c…