1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…