2 citations · 6 across the 11 of their papers we have counts for
8 papers · 1 filter
Improving Policy Learning from Delayed Interventions
Xiru Zhu, David Meger, Gregory Dudek
In this work, we explore the idea of expert intervention delays in human-supervised robotic systems for imitation learning system. Previous works in imitation learning have primari…
Tactile Modality Fusion for Vision-Language-Action Models
Charlotte Morissette, Amin Abyaneh, Wei-Di Chang +5
We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. While advances in VLAs have introduced r…
Swarm Oracle: Trustless Blockchain Agreements through Robot Swarms
Alexandre Pacheco, Hanqing Zhao, Volker Strobel +4
Blockchain consensus, rooted in the principle ``don't trust, verify'', limits access to real-world data, which may be ambiguous or inaccessible to some participants. Oracles addres…
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Faraz Lotfi, Farnoosh Faraji, Nikhil Kakodkar +3
This paper explores leveraging large language models for map-free off-road navigation using generative AI, reducing the need for traditional data collection and annotation. We prop…
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Faraz Lotfi, Khalil Virji, Nicholas Dudek +1
In this paper, we present an exploration and assessment of employing a centralized deep Q-network (DQN) controller as a substitute for the prevalent use of PID controllers in the c…
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang, Francois Hogan, Scott Fujimoto +2
In this paper, we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies. W…
Imitation Learning from Observation through Optimal Transport
Wei-Di Chang, Scott Fujimoto, David Meger +1
Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance…
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi, Khalil Virji, Farnoosh Faraji +4
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an auton…