254 citations · 1.4k across the 100 of their papers we have counts for
15 papers · 1 filter
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk +1
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. Howeve…
Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu +4
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not…
Motion Reasoning for Goal-Based Imitation Learning
De-An Huang, Yu-Wei Chao, Chris Paxton +5
We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and rep…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity
Ajay Mandlekar, Jonathan Booher, Max Spero +6
Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been c…
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Ajay Mandlekar, Fabio Ramos, Byron Boots +4
Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline l…
A Programmable Approach to Neural Network Compression
Vinu Joseph, Saurav Muralidharan, Animesh Garg +2
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Conse…
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation
Kuan Fang, Yuke Zhu, Animesh Garg +2
The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational In…
Video Interpolation and Prediction with Unsupervised Landmarks
Kevin J. Shih, Aysegul Dundar, Animesh Garg +3
Prediction and interpolation for long-range video data involves the complex task of modeling motion trajectories for each visible object, occlusions and dis-occlusions, as well as…
Controlling Assistive Robots with Learned Latent Actions
Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar +2
Assistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms use…
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin +2
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial…
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner +3
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or re…
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning
De-An Huang, Danfei Xu, Yuke Zhu +4
We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direct…
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Peter Zachares +6
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines th…
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna +6
When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects…