251 citations · 573 across the 21 of their papers we have counts for
22 papers
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169
General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…
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…
Gemini Robotics: Bringing AI into the Physical World
Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie +115
Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as…
A Taxonomy for Evaluating Generalist Robot Manipulation Policies
Jensen Gao, Suneel Belkhale, Sudeep Dasari +3
Machine learning for robot manipulation promises to unlock generalization to novel tasks and environments. But how should we measure the progress of these policies towards generali…
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…
The Ingredients for Robotic Diffusion Transformers
Sudeep Dasari, Oier Mees, Sebastian Zhao +2
In recent years roboticists have achieved remarkable progress in solving increasingly general tasks on dexterous robotic hardware by leveraging high capacity Transformer network ar…
Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation
Ria Doshi, Homer Walke, Oier Mees +2
Modern machine learning systems rely on large datasets to attain broad generalization, and this often poses a challenge in robot learning, where each robotic platform and task migh…
HRP: Human Affordances for Robotic Pre-Training
Mohan Kumar Srirama, Sudeep Dasari, Shikhar Bahl +1
In order to *generalize* to various tasks in the wild, robotic agents will need a suitable representation (i.e., vision network) that enables the robot to predict optimal actions g…
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…
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…
An Unbiased Look at Datasets for Visuo-Motor Pre-Training
Sudeep Dasari, Mohan Kumar Srirama, Unnat Jain +1
Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by…
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…
MyoDex: A Generalizable Prior for Dexterous Manipulation
Vittorio Caggiano, Sudeep Dasari, Vikash Kumar
Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled b…
Manipulate by Seeing: Creating Manipulation Controllers from Pre-Trained Representations
Jianren Wang, Sudeep Dasari, Mohan Kumar Srirama +2
The field of visual representation learning has seen explosive growth in the past years, but its benefits in robotics have been surprisingly limited so far. Prior work uses generic…
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps
Sudeep Dasari, Abhinav Gupta, Vikash Kumar
Learning diverse dexterous manipulation behaviors with assorted objects remains an open grand challenge. While policy learning methods offer a powerful avenue to attack this proble…
RB2: Robotic Manipulation Benchmarking with a Twist
Sudeep Dasari, Jianren Wang, Joyce Hong +12
Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research g…
Model-Based Visual Planning with Self-Supervised Functional Distances
Stephen Tian, Suraj Nair, Frederik Ebert +4
A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-…
Transformers for One-Shot Visual Imitation
Sudeep Dasari, Abhinav Gupta
Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex se…
RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian +6
Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the pro…
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Frederik Ebert, Chelsea Finn, Sudeep Dasari +3
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability dem…
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
Frederik Ebert, Sudeep Dasari, Alex X. Lee +2
Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for con…
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Tianhe Yu, Chelsea Finn, Annie Xie +4
Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning fr…