4 citations · 20 across the 25 of their papers we have counts for
30 papers
AnyRank: Reliable Anytime Policy Ranking via Sequential Testing
Apurva Badithela, David Snyder, Tenny Yin +6
Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of hardware trials. However, f…
Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation
Wonsuhk Jung, Sundhar Vinodh Sangeetha, Chen Xu +4
Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse, user-specified goals, yet…
The Gaussian Is Enough: Flow-Matching Priors Do Not Help When Fine-Tuning Large Behavior Models
Chen Xu, Rishi Shah, Hadas Kress-Gazit +2
Modern robot imitation learning increasingly relies on generative policies based on diffusion or flow-matching models, which generate actions by transforming samples from a prior d…
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…
Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison
David Snyder, Apurva Badithela, Nikolai Matni +4
Generalist robot manipulation policies are becoming increasingly capable, but are limited in evaluation to a small number of hardware rollouts. This strong resource constraint in r…
Impact of Different Failures on a Robot's Perceived Reliability
Andrew Violette, Zhanxin Wu, Haruki Nishimura +5
Robots fail, potentially leading to a loss in the robot's perceived reliability (PR), a measure correlated with trustworthiness. In this study we examine how various kinds of failu…
The Impact of Class Uncertainty Propagation in Perception-Based Motion Planning
Jibran Iqbal Shah, Andrei Ivanovic, Kelly Zhu +4
Autonomous vehicles (AVs) are being increasingly deployed in urban environments. In order to operate safely and reliably, AVs need to account for the inherent uncertainty associate…
Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning
Kevin Huang, Rosario Scalise, Cleah Winston +9
Imitation learning has proven effective for training robots to perform complex tasks from expert human demonstrations. However, it remains limited by its reliance on high-quality,…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…
SAFE: Multitask Failure Detection for Vision-Language-Action Models
Qiao Gu, Yuanliang Ju, Shengxiang Sun +4
While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on no…
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara +3
Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthca…
Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping
David Snyder, Asher James Hancock, Apurva Badithela +6
Imitation learning has enabled robots to perform complex, long-horizon tasks in challenging dexterous manipulation settings. As new methods are developed, they must be rigorously e…
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies
Chen Xu, Tony Khuong Nguyen, Emma Dixon +7
Recent years have witnessed impressive robotic manipulation systems driven by advances in imitation learning and generative modeling, such as diffusion- and flow-based approaches.…
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images
Kyle B. Hatch, Ashwin Balakrishna, Oier Mees +8
Image and video generative models that are pre-trained on Internet-scale data can greatly increase the generalization capacity of robot learning systems. These models can function…
Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving
Bernard Lange, Masha Itkina, Jiachen Li +1
Environment prediction frameworks are critical for the safe navigation of autonomous vehicles (AVs) in dynamic settings. LiDAR-generated occupancy grid maps (L-OGMs) offer a robust…
How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation
Joseph A. Vincent, Haruki Nishimura, Masha Itkina +3
With the rise of stochastic generative models in robot policy learning, end-to-end visuomotor policies are increasingly successful at solving complex tasks by learning from human d…
Explore until Confident: Efficient Exploration for Embodied Question Answering
Allen Z. Ren, Jaden Clark, Anushri Dixit +3
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather in…
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…
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
Masha Itkina, Mykel J. Kochenderfer
Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) d…
LOPR: Latent Occupancy PRediction using Generative Models
Bernard Lange, Masha Itkina, Mykel J. Kochenderfer
Environment prediction frameworks are integral for autonomous vehicles, enabling safe navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer a robus…
Occlusion-Aware Crowd Navigation Using People as Sensors
Ye-Ji Mun, Masha Itkina, Shuijing Liu +1
Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the highly dynamic, partially observable environment. Occlusions are highly prevalent in such set…
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
Harrison Delecki, Masha Itkina, Bernard Lange +2
Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse we…
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models
Phil Chen, Masha Itkina, Ransalu Senanayake +1
Many applications of generative models rely on the marginalization of their high-dimensional output probability distributions. Normalization functions that yield sparse probability…