11 citations · 16 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
Lujie Yang, H. J. Terry Suh, Tong Zhao +5
We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-qu…
CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational Object Graph
Yixuan Wang, Leonor Fermoselle, Tarik Kelestemur +2
Mobile exploration is a longstanding challenge in robotics, yet current methods primarily focus on active perception instead of active interaction, limiting the robot's ability to…
GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy
Yixuan Wang, Guang Yin, Binghao Huang +3
Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often…
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
Ondrej Biza, Thomas Weng, Lingfeng Sun +6
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…
Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification…
Pregrasp Object Material Classification by a Novel Gripper Design with Integrated Spectroscopy
Nathaniel Hanson, Tarik Kelestemur, Deniz Erdogmus +1
Robots benefit from being able to classify objects they interact with or manipulate based on their material properties. This capability ensures fine manipulation of complex objects…
Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation
Tarik Kelestemur, Robert Platt, Taskin Padir
Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to e…
Learning Bayes Filter Models for Tactile Localization
Tarik Kelestemur, Colin Keil, John P. Whitney +2
Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or…