96 citations · 359 across the 71 of their papers we have counts for
13 papers · 1 filter
DeepFleet: Multi-Agent Foundation Models for Mobile Robots
Ameya Agaskar, Sriram Siva, William Pickering +18
We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models are trained on fleet movement d…
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje +13
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might f…
Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots
Akanksha Saran, Kush Desai, Mai Lee Chang +3
Humans use audio signals in the form of spoken language or verbal reactions effectively when teaching new skills or tasks to other humans. While demonstrations allow humans to teac…
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta +2
Task specification is at the core of programming autonomous robots. A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate ado…
Distributional Depth-Based Estimation of Object Articulation Models
Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov +1
We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a pri…
SCAPE: Learning Stiffness Control from Augmented Position Control Experiences
Mincheol Kim, Scott Niekum, Ashish D. Deshpande
We introduce a sample-efficient method for learning state-dependent stiffness control policies for dexterous manipulation. The ability to control stiffness facilitates safe and rel…
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck +1
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to…
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty
Ajinkya Jain, Scott Niekum
Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation d…
Understanding Teacher Gaze Patterns for Robot Learning
Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz +1
Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tas…
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris
A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen subs…
LAAIR: A Layered Architecture for Autonomous Interactive Robots
Yuqian Jiang, Nick Walker, Minkyu Kim +6
When developing general purpose robots, the overarching software architecture can greatly affect the ease of accomplishing various tasks. Initial efforts to create unified robot sy…
Towards Online Learning from Corrective Demonstrations
Reymundo A. Gutierrez, Elaine Schaertl Short, Scott Niekum +1
Robots operating in real-world human environments will likely encounter task execution failures. To address this, we would like to allow co-present humans to refine the robot's tas…
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Ajinkya Jain, Scott Niekum
Noisy observations coupled with nonlinear dynamics pose one of the biggest challenges in robot motion planning. By decomposing nonlinear dynamics into a discrete set of local dynam…