Robot Learning & Embodied AI
Vision-language-action models, large-scale imitation learning, sim-to-real transfer, dexterous manipulation and humanoid control.
Most influential
- Imitation LearningArticle1,059 citationsdoi:10.1145/3054912
- Policy search for motor primitives in roboticsArticle477 citationsdoi:10.1007/s10994-010-5223-6
- PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning
Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the community's continued efforts, most state-of-the-art MAPF planners still rely on centralized planning and scale poorly past a few hundred agents. Such planning approaches are maladapted …
Article424 citationsarXiv:1809.03531doi:10.1109/lra.2019.2903261 - Learning and Reproduction of Gestures by ImitationArticle461 citationsdoi:10.1109/mra.2010.936947
- RLBench: The Robot Learning Benchmark & Learning Environment
We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks ranging in difficulty, from simple target reaching and door opening, to longer multi-stage tasks, such as opening an oven and placing a tray in it. We provide an array of both proprioceptive observatio…
Article403 citationsarXiv:1909.12271doi:10.1109/lra.2020.2974707 - An Algorithmic Perspective on Imitation Learning
As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstr…
Article405 citationsarXiv:1811.06711doi:10.1561/2300000053 - Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility …
Abstract excerpt by Jānis Ārents et al., CC-BY, https://doi.org/10.3390/app12020937; truncated.
Article324 citationsdoi:10.3390/app12020937 - Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit, we provide a suite of benchmark tasks of varying difficulty -- from single-stage cabi…
Article269 citationsarXiv:2301.04195doi:10.1109/lra.2023.3270034 - Dynamic movement primitives in robotics: A tutorial survey
Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion int…
Article267 citationsarXiv:2102.03861doi:10.1177/02783649231201196 - Human – robot interaction review and challenges on task planning and programmingArticle376 citationsdoi:10.1080/0951192x.2015.1130251
- One-Shot Imitation LearningArticle372 citations
- A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behavi…
Article229 citationsarXiv:2309.02473doi:10.1109/tcyb.2024.3395626 - One-Shot Visual Imitation Learning via Meta-Learning
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this…
Preprint341 citationsarXiv:1709.04905 - Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic InputArticle279 citationsdoi:10.1163/016918611x558261
- A Survey of Deep RL and IL for Autonomous Driving Policy Learning
Autonomous driving (AD) agents generate driving policies based on online perception results, which are obtained at multiple levels of abstraction, e.g., behavior planning, motion planning and control. Driving policies are crucial to the realization of safe, efficient and harmonious driving behaviors, where AD agents still face substantial challenges in compl…
Article240 citationsarXiv:2101.01993doi:10.1109/tits.2021.3134702
Newest work
- Tactus: Tactile & Pose Sensing Glove for Natural Dexterity Data CollectionPreprintarXiv:2610.39076
- OpenFlyV2: An onboard vision-language-action model for aerial perception and navigationPreprintarXiv:2610.39287
- SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPUPreprintarXiv:2610.39327
- UMI-Live for In-The-Wild Bimanual Robotics Data CollectionPreprintarXiv:2610.39385
- ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation LearningPreprintarXiv:2610.39429
- Smooth Action Chunking Via Movement-Primitive Representations for Imitation LearningPreprintarXiv:2610.39451
- Same Label, Different Interface: Beyond Action-Space Labels in Robot LearningPreprintarXiv:2610.38104
- Embodied at Sea: Imitation Learning for Multi-Task Underwater ManipulationPreprintarXiv:2610.38199
- CuraEval: Evaluating Demonstration Curation on Mixed-Skill Robot DataPreprintarXiv:2610.38221
- SimRetract: Zero-Shot Sim-to-Real Robotic Assistance for Retractor Take-Over in Open SurgeryPreprintarXiv:2610.38234