2.3k citations · 3.8k across the 166 of their papers we have counts for
23 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…
Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation
Jennifer Grannen, Michelle Pan, Kenneth Llontop +4
Foundation models (FM) have unlocked powerful zero-shot capabilities in vision and language, yet their reliance on internet pretraining data leaves them brittle in unstructured, re…
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn +2
While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to pr…
Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu +2
Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks. These pretrained policies, trained on large datasets, produ…
Data Retrieval with Importance Weights for Few-Shot Imitation Learning
Amber Xie, Rahul Chand, Dorsa Sadigh +1
While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environme…
EXPO: Stable Reinforcement Learning with Expressive Policies
Perry Dong, Qiyang Li, Dorsa Sadigh +1
We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Training expressive policy classes with onlin…
ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaboration
Jennifer Grannen, Siddharth Karamcheti, Blake Wulfe +1
Collaborative robots must quickly adapt to their partner's intent and preferences to proactively identify helpful actions. This is especially true in situated settings where human…
Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization
Jonathan Yang, Chuyuan Kelly Fu, Dhruv Shah +3
In this work, we investigate how spatially grounded auxiliary representations can provide both broad, high-level grounding as well as direct, actionable information to improve poli…
Scaffolding Dexterous Manipulation with Vision-Language Models
Vincent de Bakker, Joey Hejna, Tyler Ga Wei Lum +6
Dexterous robotic hands are essential for performing complex manipulation tasks, yet remain difficult to train due to the challenges of demonstration collection and high-dimensiona…
HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao +7
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained m…
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…
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
Hengyuan Hu, Aniket Das, Dorsa Sadigh +1
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant infe…
Unified Video Action Model
Shuang Li, Yihuai Gao, Dorsa Sadigh +1
A unified video and action model holds significant promise for robotics, where videos provide rich scene information for action prediction, and actions provide dynamics information…
Latent Diffusion Planning for Imitation Learning
Amber Xie, Oleh Rybkin, Dorsa Sadigh +1
Recent progress in imitation learning has been enabled by policy architectures that scale to complex visuomotor tasks, multimodal distributions, and large datasets. However, these…
Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning
Jaden Clark, Joey Hejna, Dorsa Sadigh
Expressive robotic behavior is essential for the widespread acceptance of robots in social environments. Recent advancements in learned legged locomotion controllers have enabled m…
A Study of Perceived Safety for Soft Robotics in Caregiving Tasks
Cosima du Pasquier, Jennifer Grannen, Chuer Pan +6
In this project, we focus on human-robot interaction in caregiving scenarios like bathing, where physical contact is inevitable and necessary for proper task execution because forc…
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning
Haozhuo Li, Yuchen Cui, Dorsa Sadigh
Imitation learning is a promising approach for learning robot policies with user-provided data. The way demonstrations are provided, i.e., demonstration modality, influences the qu…
Shared Autonomy for Proximal Teaching
Megha Srivastava, Reihaneh Iranmanesh, Yuchen Cui +6
Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for…
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…
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Bidipta Sarkar, Warren Xia, C. Karen Liu +1
Communicating in natural language is a powerful tool in multi-agent settings, as it enables independent agents to share information in partially observable settings and allows zero…
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…
Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning
Juntao Ren, Priya Sundaresan, Dorsa Sadigh +2
Teaching robots to autonomously complete everyday tasks remains a challenge. Imitation Learning (IL) is a powerful approach that imbues robots with skills via demonstrations, but i…