2.4k citations · 8.4k across the 129 of their papers we have counts for
19 papers · 1 filter
Learning Multi-Arm Manipulation Through Collaborative Teleoperation
Albert Tung, Josiah Wong, Ajay Mandlekar +4
Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but ha…
Human-in-the-Loop Imitation Learning using Remote Teleoperation
Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín +3
Imitation Learning is a promising paradigm for learning complex robot manipulation skills by reproducing behavior from human demonstrations. However, manipulation tasks often conta…
Topological Planning with Transformers for Vision-and-Language Navigation
Kevin Chen, Junshen K. Chen, Jo Chuang +2
Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics com…
Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search
Andrey Kurenkov, Roberto Martín-Martín, Jeff Ichnowski +2
Searching for objects in indoor organized environments such as homes or offices is part of our everyday activities. When looking for a target object, we jointly reason about the ro…
iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen, Fei Xia, Chengshu Li +12
We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interac…
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
Claudia Pérez-D'Arpino, Can Liu, Patrick Goebel +2
Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation…
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Bryan Chen, Alexander Sax, Gene Lewis +5
Vision-based robotics often separates the control loop into one module for perception and a separate module for control. It is possible to train the whole system end-to-end (e.g. w…
Deep Affordance Foresight: Planning Through What Can Be Done in the Future
Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín +3
Planning in realistic environments requires searching in large planning spaces. Affordances are a powerful concept to simplify this search, because they model what actions can be s…
Multimodal Sensor Fusion with Differentiable Filters
Michelle A. Lee, Brent Yi, Roberto Martín-Martín +2
Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities acc…
Privacy Preserving Recalibration under Domain Shift
Rachel Luo, Shengjia Zhao, Jiaming Song +3
Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recal…
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter
Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni +4
When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the o…
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair, Silvio Savarese, Chelsea Finn
Learned dynamics models combined with both planning and policy learning algorithms have shown promise in enabling artificial agents to learn to perform many diverse tasks with limi…
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Fei Xia, Chengshu Li, Roberto Martín-Martín +3
Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action s…
How Trustworthy are Performance Evaluations for Basic Vision Tasks?
Tran Thien Dat Nguyen, Hamid Rezatofighi, Ba-Ngu Vo +3
This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking…
Adaptive Procedural Task Generation for Hard-Exploration Problems
Kuan Fang, Yuke Zhu, Silvio Savarese +1
We introduce Adaptive Procedural Task Generation (APT-Gen), an approach to progressively generate a sequence of tasks as curricula to facilitate reinforcement learning in hard-expl…
Generative Sparse Detection Networks for 3D Single-shot Object Detection
JunYoung Gwak, Christopher Choy, Silvio Savarese
3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality. Yet, the sparse nature of the 3D data…
Probabilistic Visual Navigation with Bidirectional Image Prediction
Noriaki Hirose, Shun Taguchi, Fei Xia +4
Humans can robustly follow a visual trajectory defined by a sequence of images (i.e. a video) regardless of substantial changes in the environment or the presence of obstacles. We…
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín +2
Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process. However, due…
JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
Abhijeet Shenoi, Mihir Patel, JunYoung Gwak +5
Robots navigating autonomously need to perceive and track the motion of objects and other agents in its surroundings. This information enables planning and executing robust and saf…