8 papers
Selective Commitment for Language-Guided Object Retrieval under Partial Observability
Wonhee Koh, Sushil Samuel Dinesh, Hansol Ko +2
Language-guided object retrieval under partial observability requires deciding whether to gather more evidence, interact with the scene, grasp a candidate, or abstain. We present a…
FlipToSee: A Probabilistic Stable Placement Prior for Active Visual Exploration via Regrasping
Chang Shu, Sushil Samuel Dinesh, Shinkyu Park
Active visual exploration of tabletop objects often requires reorienting an unknown resting object onto a different stable support face to expose occluded surfaces. To identify suc…
V2F: Vision-Informed Grasp Force Prediction for Damage-Aware Robotic Handling of Date Fruits
Shahd Shami, Obadah Wali, Eric Feron +1
This paper presents a vision-informed grasp force prediction framework for robotic handling of date fruits. Addressing the dual challenge of high detachment forces and low bruise t…
FlipItRight: Stable Pose-Targeted Throw-Flip Across Diverse Objects
Axel Dawne, Shinkyu Park
We propose FlipItRight, a framework for stable planar pose-targeted throw-flip with a high-DoF manipulator. The task is decomposed into an object-level planner, which generates can…
RACAS: Controlling Diverse Robots With a Single Agentic System
Dylan R. Ashley, Jan Przepióra, Yimeng Chen +5
Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low-level interfaces to h…
On the Stabilization of Rigid Formations on Regular Curves
Mohamed Elobaid, Shinkyu Park, Eric Feron
This work deals with the problem of stabilizing a multi-agent rigid formation on a general class of planar curves. Namely, we seek to stabilize an equilateral polygonal formation o…
Toward Accurate Long-Horizon Robotic Manipulation: Language-to-Action with Foundation Models via Scene Graphs
Sushil Samuel Dinesh, Shinkyu Park
This paper presents a framework that leverages pre-trained foundation models for robotic manipulation without domain-specific training. The framework integrates off-the-shelf model…
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
Gokul Puthumanaillam, Jae Hyuk Song, Nurzhan Yesmagambet +2
Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinati…