2 citations · 5 across the 12 of their papers we have counts for
13 papers
TACTIC: Tactile and Vision Conditioned Contact-Centric Control for Whole-Arm Manipulation
Rishabh Madan, Angchen Xie, Samantha Saak +7
Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and brea…
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…
A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
Fanqi Lin, Kushal Arora, Jean Mercat +9
Large behavior models have shown strong dexterous manipulation capabilities by extending imitation learning to large-scale training on multi-task robot data, yet their generalizati…
Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation
Jennifer Grannen, Michelle Pan, Kenneth Llontop +4
Foundation models (FM) have unlocked powerful zero-shot capabilities in vision and language, yet their reliance on internet pretraining data leaves them brittle in unstructured, re…
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…
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Neşet Ünver Akmandor, Sarvesh Prajapati, Mark Zolotas +1
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this pap…
Chance-Constrained Convex MPC for Robust Quadruped Locomotion Under Parametric and Additive Uncertainties
Ananya Trivedi, Sarvesh Prajapati, Mark Zolotas +2
Recent advances in quadrupedal locomotion have focused on improving stability and performance across diverse environments. However, existing methods often lack adequate safety anal…
Data-Driven Sampling Based Stochastic MPC for Skid-Steer Mobile Robot Navigation
Ananya Trivedi, Sarvesh Prajapati, Anway Shirgaonkar +2
Traditional approaches to motion modeling for skid-steer robots struggle with capturing nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we t…
User-customizable Shared Control for Robot Teleoperation via Virtual Reality
Rui Luo, Mark Zolotas, Drake Moore +1
Shared control can ease and enhance a human operator's ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. Howe…
A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains
Ananya Trivedi, Mark Zolotas, Adeeb Abbas +3
Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skid…
Shared Affordance-awareness via Augmented Reality for Proactive Assistance in Human-robot Collaboration
Drake Moore, Mark Zolotas, Taskin Padir
Enabling humans and robots to collaborate effectively requires purposeful communication and an understanding of each other's affordances. Prior work in human-robot collaboration ha…
Mobile MoCap: Retroreflector Localization On-The-Go
Gary Lvov, Mark Zolotas, Nathaniel Hanson +4
Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial ma…
Disentangled Sequence Clustering for Human Intention Inference
Mark Zolotas, Yiannis Demiris
Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probabili…