Author name cluster

Eric M. Wolff

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

ICRA Conference 2025 Conference Paper

Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-Based Autonomous Driving

Yichen Xie 0002
Hongge Chen
Gregory P. Meyer
Yong Jae Lee
Eric M. Wolff
Masayoshi Tomizuka
Wei Zhan
Yuning Chai

Multi-frame temporal inputs are important for vision-based autonomous driving. Observations from different angles enable the recovery of 3 D object states from 2 D images as long as we can identify the same instance from different input frames. However, the dynamic nature of driving scenes leads to significant variance in the instance appearance and shape captured by the cameras at different time steps. To this end, we propose a novel contrastive learning algorithm, Cohere3D, to learn coherent instance representations robust to the changes of distance and perspective in a long-term temporal sequence without any human annotations. In the pretraining stage, raw point clouds from LiDAR sensors are utilized to construct the instance-wise long-term temporal correspondence, which serves as guidance for the extraction of instance-level representation from the vision-based bird's-eye-view (BEV) feature map. Cohere3D encourages consistent representation for the same instance at different frames but distinguishes between different instances. We validate the effectiveness and generalizability of our algorithm by finetuning the pretrained model across key downstream autonomous driving tasks: perception, mapping, prediction, and planning. Results show a notable improvement in both data efficiency and final performance in all these tasks.

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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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ICML Conference 2025 Conference Paper

Generative Data Mining with Longtail-Guided Diffusion

David S. Hayden
Mao Ye 0006
Timur Garipov
Gregory P. Meyer
Carl Vondrick
Zhao Chen
Yuning Chai
Eric M. Wolff

It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, data mining, and retraining. We instead develop a proactive longtail discovery process by imagining additional data during training. In particular, we develop general model-based longtail signals, including a differentiable, single forward pass formulation of epistemic uncertainty that does not impact model parameters or predictive performance but can flag rare or hard inputs. We leverage these signals as guidance to generate additional training data from a latent diffusion model in a process we call Longtail Guidance (LTG). Crucially, we can perform LTG without retraining the diffusion model or the predictive model, and we do not need to expose the predictive model to intermediate diffusion states. Data generated by LTG exhibit semantically meaningful variation, yield significant generalization improvements on numerous image classification benchmarks, and can be analyzed by a VLM to proactively discover, textually explain, and address conceptual gaps in a deployed predictive model.

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ICRA Conference 2023 Conference Paper

DriveIRL: Drive in Real Life with Inverse Reinforcement Learning

Tung Phan-Minh
Forbes Howington
Ting-Sheng Chu
Momchil S. Tomov
Robert E. Beaudoin
Sang Uk Lee
Nanxiang Li
Caglayan Dicle

In this paper, we introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals and scores them with a learned model. The best trajectory is tracked by our self-driving vehicle's low-level controller. We train our trajectory scoring model on a 500+ hour real-world dataset of expert driving demonstrations in Las Vegas within the maximum entropy IRL framework. DriveIRL's benefits include: a simple design due to only learning the trajectory scoring function, a flexible and relatively interpretable feature engineering approach, and strong real-world performance. We validated DriveIRL on the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic, including scenarios involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff zones. Our dataset, a part of nuPlan, has been released to the public to help further research in this area.

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IROS Conference 2014 Conference Paper

A compositional approach to stochastic optimal control with co-safe temporal logic specifications

Matanya B. Horowitz
Eric M. Wolff
Richard M. Murray

We introduce an algorithm for the optimal control of stochastic nonlinear systems subject to temporal logic constraints on their behavior. We compute directly on the state space of the system, avoiding the expensive pre-computation of a discrete abstraction. An automaton that corresponds to the temporal logic specification guides the computation of a control policy that maximizes the probability that the system satisfies the specification. This reduces controller synthesis to solving a sequence of stochastic constrained reachability problems. Each individual reachability problem is solved via the Hamilton-Jacobi-Bellman (HJB) partial differential equation of stochastic optimal control theory. To increase the efficiency of our approach, we exploit a class of systems where the HJB equation is linear due to structural assumptions on the noise. The linearity of the partial differential equation allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to conservatively satisfy a complex temporal logic specification.

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ICRA Conference 2014 Conference Paper

Optimization-based trajectory generation with linear temporal logic specifications

Eric M. Wolff
Ufuk Topcu
Richard M. Murray

We present a mathematical programming-based method for optimal control of discrete-time dynamical systems subject to temporal logic task specifications. We use linear temporal logic (LTL) to specify a wide range of properties and tasks, such as safety, progress, response, surveillance, repeated assembly, and environmental monitoring. Our method directly encodes an LTL formula as mixed-integer linear constraints on the continuous system variables, avoiding the computationally expensive processes of creating a finite abstraction of the system and a Büchi automaton for the specification. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (10+ continuous state) dynamical systems.

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IROS Conference 2013 Conference Paper

Automaton-guided controller synthesis for nonlinear systems with temporal logic

Eric M. Wolff
Ufuk Topcu
Richard M. Murray

We develop a method for the control of discrete-time nonlinear systems subject to temporal logic specifications. Our approach uses a coarse abstraction of the system and an automaton representing the temporal logic specification to guide the search for a feasible trajectory. This decomposes the search for a feasible trajectory into a series of constrained reachability problems. Thus, one can create controllers for any system for which techniques exist to compute (approximate) solutions to constrained reachability problems. Representative techniques include sampling-based methods for motion planning, reachable set computations for linear systems, and graph search for finite discrete systems. Our approach avoids the expensive computation of a discrete abstraction, and its implementation is amenable to parallel computing. We demonstrate our approach with numerical experiments on temporal logic motion planning problems with high-dimensional (10+ states) continuous systems.

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ICRA Conference 2013 Conference Paper

Efficient reactive controller synthesis for a fragment of linear temporal logic

Eric M. Wolff
Ufuk Topcu
Richard M. Murray

Motivated by robotic motion planning, we develop a framework for control policy synthesis for both non-deterministic transition systems and Markov decision processes that are subject to temporal logic task specifications. We introduce a fragment of linear temporal logic that can be used to specify common motion planning tasks such as safe navigation, response to the environment, persistent coverage, and surveillance. This fragment is computationally efficient; the complexity of control policy synthesis is a doubly-exponential improvement over standard linear temporal logic for both non-deterministic transition systems and Markov decision processes. This improvement is possible because we compute directly on the original system, as opposed to the automata-based approach commonly used. We give simulation results for representative motion planning tasks and compare to generalized reactivity(1).

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