17 citations · 38 across the 11 of their papers we have counts for
14 papers
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…
The Surprising Difficulty of Search in Model-Based Reinforcement Learning
Wei-Di Chang, Mikael Henaff, Brandon Amos +2
This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for…
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk, Wei-Di Chang, Gregory Dudek +1
We investigate the integration of attention maps from a pre-trained Vision Transformer into voxel representations to enhance bimanual robotic manipulation. Specifically, we extract…
EMoE: Training-Free Expert Disagreement for Uncertainty-Aware Text-to-Image Diffusion
Lucas Berry, Axel Brando, Wei-Di Chang +2
Large text-to-image diffusion models rarely expose reliable signals of when a prompt is likely to produce a poorly aligned generation, especially when training data is undisclosed.…
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…
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto, Wei-Di Chang, Edward J. Smith +3
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states,…
Learning Capacity: A Measure of the Effective Dimensionality of a Model
Daiwei Chen, Wei-Kai Chang, Pratik Chaudhari
We use a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to study a quantity called ``learning…
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
Igor Kozlov, Dmitriy Rivkin, Wei-Di Chang +3
Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). S…
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang, Juan Camilo Gamboa Higuera, Scott Fujimoto +2
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-…
One-Shot Informed Robotic Visual Search in the Wild
Karim Koreitem, Florian Shkurti, Travis Manderson +3
We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that…
Underwater Multi-Robot Convoying using Visual Tracking by Detection
Florian Shkurti, Wei-Di Chang, Peter Henderson +7
We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method…
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon +3
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for…
Benchmark Environments for Multitask Learning in Continuous Domains
Peter Henderson, Wei-Di Chang, Florian Shkurti +3
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In disc…