2.3k citations · 2.3k across the 18 of their papers we have counts for
11 papers · 1 filter
RoboCade: Gamifying Robot Data Collection
Suvir Mirchandani, Mia Tang, Jiafei Duan +3
Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstration datasets is costly: it often r…
Training Strategies for Efficient Embodied Reasoning
William Chen, Suneel Belkhale, Suvir Mirchandani +4
Robot chain-of-thought reasoning (CoT) -- wherein a model predicts helpful intermediate representations before choosing actions -- provides an effective method for improving the ge…
What Matters for Batch Online Reinforcement Learning in Robotics?
Perry Dong, Suvir Mirchandani, Dorsa Sadigh +1
The ability to learn from large batches of autonomously collected data for policy improvement -- a paradigm we refer to as batch online reinforcement learning -- holds the promise…
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…
Action-Free Reasoning for Policy Generalization
Jaden Clark, Suvir Mirchandani, Dorsa Sadigh +1
End-to-end imitation learning offers a promising approach for training robot policies. However, generalizing to new settings remains a significant challenge. Although large-scale r…
Robot Data Curation with Mutual Information Estimators
Joey Hejna, Suvir Mirchandani, Ashwin Balakrishna +7
The performance of imitation learning policies often hinges on the datasets with which they are trained. Consequently, investment in data collection for robotics has grown across b…
Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration
Jennifer Grannen, Siddharth Karamcheti, Suvir Mirchandani +2
We introduce Vocal Sandbox, a framework for enabling seamless human-robot collaboration in situated environments. Systems in our framework are characterized by their ability to ada…
So You Think You Can Scale Up Autonomous Robot Data Collection?
Suvir Mirchandani, Suneel Belkhale, Joey Hejna +3
A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling aut…
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…
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…
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
Pierre Sermanet, Tianli Ding, Jeffrey Zhao +18
We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher t…