Author name cluster

Wei-Di Chang

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7 papers

2 author rows

ICRA Conference 2025 Conference Paper

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang
Francois Robert Hogan
Scott Fujimoto
David Meger
Gregory Dudek

In this paper, we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies. We introduce DVK, an imitation learning algorithm that leverages rich pre-trained Visual Transformer patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into groups associated with semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We demonstrate how this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. To facilitate further study of generalization in Imitation Learning, all of our code for the method and evaluation, as well as the dataset, is made available.

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RLC Conference 2024 Conference Paper

Imitation Learning from Observation through Optimal Transport

Wei-Di Chang
Scott Fujimoto
David Meger
Gregory Dudek

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 of demonstrated actions. In this paper, we re-examine optimal transport for IL, in which a reward is generated based on the Wasserstein distance between the state trajectories of the learner and expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. Unlike many other state-of-the-art methods, our approach can be integrated with any RL algorithm and is amenable to ILfO. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting, achieving expert-level performance across a range of evaluation domains even when observing only a single expert trajectory without actions.

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RLJ Journal 2024 Journal Article

Imitation Learning from Observation through Optimal Transport

Wei-Di Chang
Scott Fujimoto
David Meger
Gregory Dudek

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NeurIPS Conference 2023 Conference Paper

For SALE: State-Action Representation Learning for Deep Reinforcement Learning

Scott Fujimoto
Wei-Di Chang
Edward Smith
Shixiang (Shane) Gu
Doina Precup
David Meger

In reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276. 7% and 50. 7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.

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IROS Conference 2020 Conference Paper

One-Shot Informed Robotic Visual Search in the Wild

Karim Koreitem
Florian Shkurti
Travis Manderson
Wei-Di Chang
Juan Camilo Gamboa Higuera
Gregory Dudek

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 currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the robot does not have a model of what the scientist deems to be relevant visual observations. Thus, the robot can neither visually search for particular types of objects, nor focus its attention on parts of the scene that might be more relevant than the pre-specified waypoints and viewpoints. In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection. We propose and evaluate a weakly supervised video representation learning method that outperforms ImageNet embeddings for similarity tasks in the underwater domain. We also demonstrate the deployment of this similarity operator during informed visual navigation in collaborative environmental monitoring scenarios, in large-scale field trials, where the robot and a human scientist collaboratively search for relevant visual content. Code: https://github.com/rvl-lab-utoronto/visual_search_in_the_wild.

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AAAI Conference 2018 Conference Paper

OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning

Peter Henderson
Wei-Di Chang
Pierre-Luc Bacon
David Meger
Joelle Pineau
Doina Precup

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 intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is speciﬁcally designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows signiﬁcant performance increases in one-shot transfer learning.

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IROS Conference 2017 Conference Paper

Underwater multi-robot convoying using visual tracking by detection

Florian Shkurti
Wei-Di Chang
Peter Henderson 0002
Md Jahidul Islam
Juan Camilo Gamboa Higuera
Jimmy Li 0001
Travis Manderson
Anqi Xu 0003

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 is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i. e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's independent motion.

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