2 citations · 3 across the 8 of their papers we have counts for
9 papers
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Jianing Qian, Qinhe Peng, Emmanuel Panov +4
Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due t…
HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning
An Dang, Jayjun Lee, Mustafa Mukadam +4
In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image re…
Using Temperature Sampling to Effectively Train Robot Learning Policies on Imbalanced Datasets
Basavasagar Patil, Sydney Belt, Jayjun Lee +2
Increasingly large datasets of robot actions and sensory observations are being collected to train ever-larger neural networks. These datasets are collected based on tasks and whil…
ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis
Yun Chang, Leonor Fermoselle, Duy Ta +3
While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract,…
Task-Oriented Hierarchical Object Decomposition for Visuomotor Control
Jianing Qian, Yunshuang Li, Bernadette Bucher +1
Good pre-trained visual representations could enable robots to learn visuomotor policy efficiently. Still, existing representations take a one-size-fits-all-tasks approach that com…
Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation
Sonia Raychaudhuri, Duy Ta, Katrina Ashton +3
Large scale scenes such as multifloor homes can be robustly and efficiently mapped with a 3D graph of landmarks estimated jointly with robot poses in a factor graph, a technique co…
Continuously Improving Mobile Manipulation with Autonomous Real-World RL
Russell Mendonca, Emmanuel Panov, Bernadette Bucher +2
We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1)…
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Naoki Yokoyama, Sehoon Ha, Dhruv Batra +2
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like sea…
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma +7
Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terra…