14 citations · 91 across the 26 of their papers we have counts for
27 papers
Freeform Preference Learning for Robotic Manipulation
Marcel Torne, Anubha Mahajan, Abhijnya Bhat +1
Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little sig…
RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
Yinpei Dai, Hongze Fu, Jayjun Lee +6
Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipulating objects that become temporarily o…
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…
: a Vision-Language-Action Model with Open-World Generalization
Physical Intelligence, Kevin Black, Noah Brown +33
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated im…
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
Lucy Xiaoyang Shi, Brian Ichter, Michael Equi +12
Generalist robots that can perform a range of different tasks in open-world settings must be able to not only reason about the steps needed to accomplish their goals, but also proc…
FAST: Efficient Action Tokenization for Vision-Language-Action Models
Karl Pertsch, Kyle Stachowicz, Brian Ichter +6
Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behav…
RoboCrowd: Scaling Robot Data Collection through Crowdsourcing
Suvir Mirchandani, David D. Yuan, Kaylee Burns +4
In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large q…
ALOHA Unleashed: A Simple Recipe for Robot Dexterity
Tony Z. Zhao, Jonathan Tompson, Danny Driess +4
Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learn…
: A Vision-Language-Action Flow Model for General Robot Control
Kevin Black, Noah Brown, Danny Driess +21
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artif…
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall +4
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder str…
Affordance-Guided Reinforcement Learning via Visual Prompting
Olivia Y. Lee, Annie Xie, Kuan Fang +2
Robots equipped with reinforcement learning (RL) have the potential to learn a wide range of skills solely from a reward signal. However, obtaining a robust and dense reward signal…
Robotic Control via Embodied Chain-of-Thought Reasoning
Michał Zawalski, William Chen, Karl Pertsch +3
A key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown tha…
Octo: An Open-Source Generalist Robot Policy
Octo Model Team, Dibya Ghosh, Homer Walke +16
Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies…
Evaluating Real-World Robot Manipulation Policies in Simulation
Xuanlin Li, Kyle Hsu, Jiayuan Gu +13
The field of robotics has made significant advances towards generalist robot manipulation policies. However, real-world evaluation of such policies is not scalable and faces reprod…
Efficient Imitation Learning with Conservative World Models
Victor Kolev, Rafael Rafailov, Kyle Hatch +2
We tackle the problem of policy learning from expert demonstrations without a reward function. A central challenge in this space is that these policies fail upon deployment due to…
ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
ALOHA 2 Team, Jorge Aldaco, Travis Armstrong +23
Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robus…
Yell At Your Robot: Improving On-the-Fly from Language Corrections
Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao +5
Hierarchical policies that combine language and low-level control have been shown to perform impressively long-horizon robotic tasks, by leveraging either zero-shot high-level plan…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Jensen Gao, Annie Xie, Ted Xiao +2
Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broa…
Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation
Jonathan Yang, Catherine Glossop, Arjun Bhorkar +5
Recent years in robotics and imitation learning have shown remarkable progress in training large-scale foundation models by leveraging data across a multitude of embodiments. The s…
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
Soroush Nasiriany, Fei Xia, Wenhao Yu +20
Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interactio…
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents
Michael Ahn, Debidatta Dwibedi, Chelsea Finn +24
Foundation models that incorporate language, vision, and more recently actions have revolutionized the ability to harness internet scale data to reason about useful tasks. However,…
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Jiayuan Gu, Sean Kirmani, Paul Wohlhart +14
Generalization remains one of the most important desiderata for robust robot learning systems. While recently proposed approaches show promise in generalization to novel objects, s…
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid +3
Inspired by the success of transfer learning in computer vision, roboticists have investigated visual pre-training as a means to improve the learning efficiency and generalization…