Author name cluster

Mark Edmonds

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5 papers

2 author rows

ICML Conference 2025 Conference Paper

DriveGPT: Scaling Autoregressive Behavior Models for Driving

Xin Huang
Eric M. Wolff
Paul Vernaza
Tung Phan-Minh
Hongge Chen
David S. Hayden
Mark Edmonds
Brian Pierce

We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute. We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling.

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AAAI Conference 2020 Conference Paper

Theory-Based Causal Transfer:Integrating Instance-Level Induction and Abstract-Level Structure Learning

Mark Edmonds
Xiaojian Ma
Siyuan Qi
Yixin Zhu
Hongjing Lu
Song-Chun Zhu

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two basic yet general theories for transfer learning: (i) a task shares a common abstract structure that is invariant across domains, and (ii) the behavior of speciﬁc features of the environment remain constant across domains. We adopt a Bayesian perspective of causal theory induction and use these theories to transfer knowledge between environments. Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment. A hierarchy of Bayesian structures is used to model abstract-level structural causal knowledge, and an instance-level associative learning scheme learns which speciﬁc objects can be used to induce state changes through interaction. This model-learning scheme is then integrated with a model-based planner to achieve a task in the Open- Lock environment, a virtual “escape room” with a complex hierarchy that requires agents to reason about an abstract, generalized causal structure. We compare performances against a set of predominate model-free reinforcement learning (RL) algorithms. RL agents showed poor ability transferring learned knowledge across different trials. Whereas the proposed model revealed similar performance trends as human learners, and more importantly, demonstrated transfer behavior across trials and learning situations. 1

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ICRA Conference 2018 Conference Paper

Unsupervised Learning of Hierarchical Models for Hand-Object Interactions

Xu Xie 0001
Hangxin Liu
Mark Edmonds
Feng Gao 0013
Siyuan Qi
Yixin Zhu 0001
Brandon Rothrock
Song-Chun Zhu

Contact forces of the hand are visually unobservable, but play a crucial role in understanding hand-object interactions. In this paper, we propose an unsupervised learning approach for manipulation event segmentation and manipulation event parsing. The proposed framework incorporates hand pose kinematics and contact forces using a low-cost easy-to-replicate tactile glove. We use a temporal grammar model to capture the hierarchical structure of events, integrating extracted force vectors from the raw sensory input of poses and forces. The temporal grammar is represented as a temporal And-Or graph (T-AOG), which can be induced in an unsupervised manner. We obtain the event labeling sequences by measuring the similarity between segments using the Dynamic Time Alignment Kernel (DTAK). Experimental results show that our method achieves high accuracy in manipulation event segmentation, recognition and parsing by utilizing both pose and force data.

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IROS Conference 2017 Conference Paper

A glove-based system for studying hand-object manipulation via joint pose and force sensing

Hangxin Liu
Xu Xie 0001
Matt Millar
Mark Edmonds
Feng Gao 0013
Yixin Zhu 0001
Veronica J. Santos
Brandon Rothrock

We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a sensory glove that is capable of jointly collecting data of finger poses, hand poses, as well as forces on palm and each phalanx. Specifically, the sensory glove employs a network of 15 IMUs to measure the rotations between individual phalanxes. Hand pose is then reconstructed using forward kinematics. Contact forces on the palm and each phalanx are measured by 6 customized force sensors made from Velostat, a piezoresistive material whose force-voltage relation is investigated. We further develop an open-source software pipeline consisting of drivers and processing code and a system for visualizing hand actions that is compatible with the popular Raspberry Pi architecture. In our experiment, we conduct a series of evaluations that quantitatively characterize both individual sensors and the overall system, proving the effectiveness of the proposed design.

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IROS Conference 2017 Conference Paper

Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles

Mark Edmonds
Feng Gao 0013
Xu Xie 0001
Hangxin Liu
Siyuan Qi
Yixin Zhu 0001
Brandon Rothrock
Song-Chun Zhu

Learning complex robot manipulation policies for real-world objects is challenging, often requiring significant tuning within controlled environments. In this paper, we learn a manipulation model to execute tasks with multiple stages and variable structure, which typically are not suitable for most robot manipulation approaches. The model is learned from human demonstration using a tactile glove that measures both hand pose and contact forces. The tactile glove enables observation of visually latent changes in the scene, specifically the forces imposed to unlock the child-safety mechanisms of medicine bottles. From these observations, we learn an action planner through both a top-down stochastic grammar model (And-Or graph) to represent the compositional nature of the task sequence and a bottom-up discriminative model from the observed poses and forces. These two terms are combined during planning to select the next optimal action. We present a method for transferring this human-specific knowledge onto a robot platform and demonstrate that the robot can perform successful manipulations of unseen objects with similar task structure.

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