2.3k citations · 4.7k across the 84 of their papers we have counts for
12 papers · 1 filter
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Rika Antonova, Jingyun Yang, Priya Sundaresan +3
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise def…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
Vision-Only Robot Navigation in a Neural Radiance World
Michal Adamkiewicz, Timothy Chen, Adam Caccavale +4
Neural Radiance Fields (NeRFs) have recently emerged as a powerful paradigm for the representation of natural, complex 3D scenes. NeRFs represent continuous volumetric density and…
TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation
Claire Chen, Preston Culbertson, Marion Lepert +2
Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous manipu…
Grounding Predicates through Actions
Toki Migimatsu, Jeannette Bohg
Symbols representing abstract states such as "dish in dishwasher" or "cup on table" allow robots to reason over long horizons by hiding details unnecessary for high-level planning.…
Learning Periodic Tasks from Human Demonstrations
Jingyun Yang, Junwu Zhang, Connor Settle +3
We develop a method for learning periodic tasks from visual demonstrations. The core idea is to leverage periodicity in the policy structure to model periodic aspects of the tasks.…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Learning Latent Actions to Control Assistive Robots
Dylan P. Losey, Hong Jun Jeon, Mengxi Li +5
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use…
XIRL: Cross-embodiment Inverse Reinforcement Learning
Kevin Zakka, Andy Zeng, Pete Florence +3
We investigate the visual cross-embodiment imitation setting, in which agents learn policies from videos of other agents (such as humans) demonstrating the same task, but with star…
Differentiable Factor Graph Optimization for Learning Smoothers
Brent Yi, Michelle A. Lee, Alina Kloss +2
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-des…
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
Yifan You, Lin Shao, Toki Migimatsu +1
In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability ha…
Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks
Peter A. Zachares, Michelle A. Lee, Wenzhao Lian +1
A key challenge towards the goal of multi-part assembly tasks is finding robust sensorimotor control methods in the presence of uncertainty. In contrast to previous works that rely…