103 citations · 117 across the 9 of their papers we have counts for
12 papers
Memory Anchors for Continual Robot Learning
Maximilian Du, Zhanyi Sun, Chen Xu +3
Robot policies deployed in the wild should have the capability to continually learn new tasks without forgetting existing behaviors. A common approach to combat such catastrophic f…
Will People Enjoy a Robot Trainer? A Case Study with Snoopie the Pacerbot
Maximilian Du, Jennifer Grannen, Shuran Song +1
The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonstrate an exercise, and give i…
DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Maximilian Du, Shuran Song
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, req…
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…
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling
Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie +3
Predicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. Yet, its…
To Err is Robotic: Rapid Value-Based Trial-and-Error during Deployment
Maximilian Du, Alexander Khazatsky, Tobias Gerstenberg +1
When faced with a novel scenario, it can be hard to succeed on the first attempt. In these challenging situations, it is important to know how to retry quickly and meaningfully. Re…
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…
BridgeData V2: A Dataset for Robot Learning at Scale
Homer Walke, Kevin Black, Abraham Lee +11
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096…
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets
Maximilian Du, Suraj Nair, Dorsa Sadigh +1
Enabling robots to learn novel visuomotor skills in a data-efficient manner remains an unsolved problem with myriad challenges. A popular paradigm for tackling this problem is thro…
Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning
Maximilian Du, Olivia Y. Lee, Suraj Nair +1
Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such task…
Improving LSTM Neural Networks for Better Short-Term Wind Power Predictions
Maximilian Du
This paper improves wind power prediction via weather forecast-contextualized Long Short-Term Memory Neural Network (LSTM) models. Initially, only wind power data was fed to a gene…
Application of Autoencoder-Assisted Recurrent Neural Networks to Prevent Cases of Sudden Infant Death Syndrome
Maximilian Du
This project develops and trains a Recurrent Neural Network (RNN) that monitors sleeping infants from an auxiliary microphone for cases of Sudden Infant Death Syndrome (SIDS), mani…