103 citations · 169 across the 20 of their papers we have counts for
23 papers
Steertuning Policies from a Few Human Videos
Billy Yan, Isaac Tu, Kelly Lee +8
Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce Steertuning, a system that ena…
Patch Policy: Efficient Embodied Control via Dense Visual Representations
Gaoyue Zhou, Zichen Jeff Cui, Ada Langford +3
Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning. Modern robot policies either compress each observation…
Human Universal Grasping
Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu +5
Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from hum…
Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
Xinqi Lucas Liu, Ruoxi Hu, Alejandro Ojeda Olarte +6
Lack of accessible and dexterous robot hardware has been a significant bottleneck to achieving human-level dexterity in robots. Last year, we released Ruka, a fully open-sourced, t…
Quality over Quantity: Demonstration Curation via Influence Functions for Data-Centric Robot Learning
Haeone Lee, Taywon Min, Junsu Kim +4
Learning from demonstrations has emerged as a promising paradigm for end-to-end robot control, particularly when scaled to diverse and large datasets. However, the quality of demon…
YOR: Your Own Mobile Manipulator for Generalizable Robotics
Manan H Anjaria, Mehmet Enes Erciyes, Vedant Ghatnekar +11
Recent advances in robot learning have generated significant interest in capable platforms that may eventually approach human-level competence. This interest, combined with the com…
Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
Zichen Jeff Cui, Omar Rayyan, Haritheja Etukuru +16
The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this appr…
Point Bridge: 3D Representations for Cross Domain Policy Learning
Siddhant Haldar, Lars Johannsmeier, Lerrel Pinto +4
Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulatio…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Point Policy: Unifying Observations and Actions with Key Points for Robot Manipulation
Siddhant Haldar, Lerrel Pinto
Building robotic agents capable of operating across diverse environments and object types remains a significant challenge, often requiring extensive data collection. This is partic…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies
Mara Levy, Siddhant Haldar, Lerrel Pinto +1
Developing generalizable robot policies that can robustly handle varied environmental conditions and object instances remains a fundamental challenge in robot learning. While consi…
DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation
Peiqi Liu, Zhanqiu Guo, Mohit Warke +4
Significant progress has been made in open-vocabulary mobile manipulation, where the goal is for a robot to perform tasks in any environment given a natural language description. H…
DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
Gaoyue Zhou, Hengkai Pan, Yann LeCun +1
The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remains challenging…
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
Zichen Jeff Cui, Hengkai Pan, Aadhithya Iyer +2
Imitation learning has proven to be a powerful tool for training complex visuomotor policies. However, current methods often require hundreds to thousands of expert demonstrations…
Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments
Haritheja Etukuru, Norihito Naka, Zijin Hu +7
Robot models, particularly those trained with large amounts of data, have recently shown a plethora of real-world manipulation and navigation capabilities. Several independent effo…
BAKU: An Efficient Transformer for Multi-Task Policy Learning
Siddhant Haldar, Zhuoran Peng, Lerrel Pinto
Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, w…
OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation
Aadhithya Iyer, Zhuoran Peng, Yinlong Dai +4
Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in m…
Behavior Generation with Latent Actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru +3
Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires mod…
OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics
Peiqi Liu, Yaswanth Orru, Jay Vakil +3
Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language querie…
On Bringing Robots Home
Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru +4
Throughout history, we have successfully integrated various machines into our homes. Dishwashers, laundry machines, stand mixers, and robot vacuums are a few recent examples. Howev…
PolyTask: Learning Unified Policies through Behavior Distillation
Siddhant Haldar, Lerrel Pinto
Unified models capable of solving a wide variety of tasks have gained traction in vision and NLP due to their ability to share regularities and structures across tasks, which impro…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…