62 citations · 206 across the 36 of their papers we have counts for
37 papers
Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
Haonan Chang, Kowndinya Boyalakuntla, Shiyang Lu +8
We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-base…
LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
Haonan Chang, Kai Gao, Kowndinya Boyalakuntla +5
We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within…
Detect Everything with Few Examples
Xinyu Zhang, Yuhan Liu, Yuting Wang +1
Few-shot object detection aims at detecting novel categories given only a few example images. It is a basic skill for a robot to perform tasks in open environments. Recent methods…
Optical Flow boosts Unsupervised Localization and Segmentation
Xinyu Zhang, Abdeslam Boularias
Unsupervised localization and segmentation are long-standing robot vision challenges that describe the critical ability for an autonomous robot to learn to decompose images into in…
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos
Shiyang Lu, Yunfu Deng, Abdeslam Boularias +1
This work proposes a self-supervised learning system for segmenting rigid objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which…
Mono-STAR: Mono-camera Scene-level Tracking and Reconstruction
Haonan Chang, Dhruv Metha Ramesh, Shijie Geng +2
We present Mono-STAR, the first real-time 3D reconstruction system that simultaneously supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological…
Scene-level Tracking and Reconstruction without Object Priors
Haonan Chang, Abdeslam Boularias
We present the first real-time system capable of tracking and reconstructing, individually, every visible object in a given scene, without any form of prior on the rigidness of the…
Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs
Junchi Liang, Abdeslam Boularias
This paper presents a new technique for learning category-level manipulation from raw RGB-D videos of task demonstrations, with no manual labels or annotations. Category-level lear…
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning
Baichuan Huang, Abdeslam Boularias, Jingjin Yu
We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree searc…
USHER: Unbiased Sampling for Hindsight Experience Replay
Liam Schramm, Yunfu Deng, Edgar Granados +1
Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for…
Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter
Baichuan Huang, Teng Guo, Abdeslam Boularias +1
In this study, working with the task of object retrieval in clutter, we have developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable…
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations
Junchi Liang, Bowen Wen, Kostas Bekris +1
This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demons…
Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
Andrew S. Morgan, Bowen Wen, Junchi Liang +3
Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatibl…
Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement
Baichuan Huang, Shuai D. Han, Jingjin Yu +1
This paper considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clu…
A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs
Juntao Tan, Changkyu Song, Abdeslam Boularias
This paper presents a new self-supervised system for learning to detect novel and previously unseen categories of objects in images. The proposed system receives as input several u…
DIPN: Deep Interaction Prediction Network with Application to Clutter Removal
Baichuan Huang, Shuai D. Han, Abdeslam Boularias +1
We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex interactions that ensue as a robot end-effector pushes multiple objects, whose physical prop…
Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics
Yanshi Luo, Abdeslam Boularias, Mridul Aanjaneya
We present the design of a low-cost wheeled mobile robot, and an analytical model for predicting its motion under the influence of motor torques and friction forces. Using our prop…
A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning
Changkyu Song, Abdeslam Boularias
This paper introduces a new technique for learning probabilistic models of mass and friction distributions of unknown objects, and performing robust sliding actions by using the le…
Learning Transition Models with Time-delayed Causal Relations
Junchi Liang, Abdeslam Boularias
This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data…
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
Chaitanya Mitash, Rahul Shome, Bowen Wen +2
Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for m…
Learning to Slide Unknown Objects with Differentiable Physics Simulations
Changkyu Song, Abdeslam Boularias
We propose a new technique for pushing an unknown object from an initial configuration to a goal configuration with stability constraints. The proposed method leverages recent prog…
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
Liam Schramm, Avishai Sintov, Abdeslam Boularias
Transfer learning is a popular approach to bypassing data limitations in one domain by leveraging data from another domain. This is especially useful in robotics, as it allows prac…
Identifying Mechanical Models through Differentiable Simulations
Changkyu Song, Abdeslam Boularias
This paper proposes a new method for manipulating unknown objects through a sequence of non-prehensile actions that displace an object from its initial configuration to a given goa…
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
Chaitanya Mitash, Bowen Wen, Kostas Bekris +1
This paper introduces key machine learning operations that allow the realization of robust, joint 6D pose estimation of multiple instances of objects either densely packed or in un…