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Ian Abraham

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12 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Accelerating Visual-Policy Learning through Parallel Differentiable Simulation

Haoxiang You
Yilang Liu
Ian Abraham

In this work, we propose a computationally efficient algorithm for visual policy learning that leverages differentiable simulation and first-order analytical policy gradients. Our approach decouple the rendering process from the computation graph, enabling seamless integration with existing differentiable simulation ecosystems without the need for specialized differentiable rendering software. This decoupling not only reduces computational and memory overhead but also effectively attenuates the policy gradient norm, leading to more stable and smoother optimization. We evaluate our method on standard visual control benchmarks using modern GPU-accelerated simulation. Experiments show that our approach significantly reduces wall-clock training time and consistently outperforms all baseline methods in terms of final returns. Notably, on complex tasks such as humanoid locomotion, our method achieves a $4\times$ improvement in final return, and successfully learns a humanoid running policy within 4 hours on a single GPU. Videos and code are available on https: //haoxiangyou. github. io/Dva_website

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NeurIPS Conference 2025 Conference Paper

Diversifying Parallel Ergodic Search: A Signature Kernel Evolution Strategy

Sreevardhan Sirigiri
Christian Hughes
Ian Abraham
Fabio Ramos

Effective robotic exploration in continuous domains requires planning trajectories that maximize coverage over a predefined region. A recent development, Stein Variational Ergodic Search (SVES), proposed parallel ergodic exploration (a key approach within the field of robotic exploration), via Stein variational inference that computes a set of candidate trajectories approximating the posterior distribution over the solution space trajectories. While this approach leverages GPU parallelism well, the trajectories in the set might not be distinct enough, leading to a suboptimal set. In this paper, we propose two key methods to diversify the solution set of this approach. First, we leverage the signature kernel within the SVES framework, introducing a pathwise, sequence-sensitive interaction that preserves the Markovian structure of the trajectories and naturally spreads paths across distinct regions of the search space. Second, we propose a derivative-free evolution-strategy interpretation of SVES that exploits batched, GPU-friendly fitness evaluations and can be paired with approximate gradients whenever analytic gradients of the kernel are unavailable or computationally intractable. The resulting method both retains SVES’s advantages while diversifying the solution set and extending its reach to black-box objectives. Across planar forest search, 3D quadrotor coverage, and model-predictive control benchmarks, our approach consistently reduces ergodic cost and produces markedly richer trajectory sets than SVES without significant extra tuning effort.

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ICRA Conference 2025 Conference Paper

Ergodic Exploration over Meshable Surfaces

Dayi Dong
Albert Xu
Geordan Gutow
Howie Choset
Ian Abraham

Robotic search and rescue, exploration, and inspection require trajectory planning across a variety of domains. A popular approach to trajectory planning for these types of missions is ergodic search, which biases a trajectory to spend time in parts of the exploration domain that are believed to contain more information. Most prior work on ergodic search has been limited to searching simple surfaces, like a 2D Euclidean plane or a sphere, as they rely on projecting functions defined on the exploration domain onto analytically obtained Fourier basis functions. In this paper, we extend ergodic search to any surface that can be approximated by a triangle mesh. The basis functions are approximated through finite element methods on a triangle mesh of the domain. We formally prove that this approximation converges to the continuous case as the mesh approximation converges to the true domain. We demonstrate that on domains where analytical basis functions are available (plane, sphere), the proposed method obtains equivalent results, and while on other domains (torus, bunny, wind turbine), the approach is versatile enough to still search effectively. Lastly, we also compare with an existing ergodic search technique that can handle complex domains and show that our method results in a higher quality exploration.

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ICRA Conference 2025 Conference Paper

Ergodic Trajectory Optimization on Generalized Domains Using Maximum Mean Discrepancy

Christian Hughes
Houston Warren
Darrick Lee
Fabio Ramos 0001
Ian Abraham

We present a novel formulation of ergodic trajectory optimization that can be specified over general domains using kernel maximum mean discrepancy. Ergodic trajectory optimization is an effective approach that generates coverage paths for problems related to robotic inspection, information gathering problems, and search and rescue. These optimization schemes compel the robot to spend time in a region proportional to the expected utility of visiting that region. Current methods for ergodic trajectory optimization rely on domain-specific knowledge, e. g. , a defined utility map, and well-defined spatial basis functions to produce ergodic trajectories. Here, we present a generalization of ergodic trajectory optimization based on maximum mean discrepancy that requires only samples from the search domain. We demonstrate the ability of our approach to produce coverage trajectories on a variety of problem domains including robotic inspection of objects with differential kinematics constraints and on Lie groups without having access to domain specific knowledge. Furthermore, we show favorable computational scaling compared to existing state-of-the-art methods for ergodic trajectory optimization with a trade-off between domain specific knowledge and computational scaling, thus extending the versatility of ergodic coverage on a wider application domain.

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ICRA Conference 2025 Conference Paper

Multi-Agent Ergodic Exploration Under Smoke-Based Time-Varying Sensor Visibility Constraints

Elena Wittemyer
Ananya Rao
Ian Abraham
Howie Choset

In this work, we consider the problem of multiagent informative path planning (IPP) for robots whose sensor visibility continuously changes as a consequence of a time-varying natural phenomenon. We leverage ergodic trajectory optimization (ETO), which generates paths such that the amount of time an agent spends in an area is proportional to the expected information in that area. We focus specifically on the problem of multi-agent drone search of a wildfire, where we use the time-varying environmental process of smoke diffusion to construct a sensor visibility model. This sensor visibility model is used to repeatedly calculate an expected information distribution (EID) to be used in the ETO algorithm. Our experiments show that our exploration method achieves improved information gathering over both baseline search methods and naive ergodic search formulations.

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ICRA Conference 2024 Conference Paper

Energy-Aware Ergodic Search: Continuous Exploration for Multi-Agent Systems with Battery Constraints

Adam Seewald
Cameron J. Lerch
Marvin Chancán
Aaron M. Dollar
Ian Abraham

Continuous exploration without interruption is important in scenarios such as search and rescue and precision agriculture, where consistent presence is needed to detect events over large areas. Ergodic search already derives continuous trajectories in these scenarios so that a robot spends more time in areas with high information density. However, existing literature on ergodic search does not consider the robot's energy constraints, limiting how long a robot can explore. In fact, if the robots are battery-powered, it is physically not possible to continuously explore on a single battery charge. Our paper tackles this challenge, integrating ergodic search methods with energy-aware coverage. We trade off battery usage and coverage quality, maintaining uninterrupted exploration by at least one agent. Our approach derives an abstract battery model for future state-of-charge estimation and extends canonical ergodic search to ergodic search under battery constraints. Empirical data from simulations and real-world experiments demonstrate the effectiveness of our energy-aware ergodic search, which ensures continuous exploration and guarantees spatial coverage.

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ICRA Conference 2024 Conference Paper

RB5 Low-Cost Explorer: Implementing Autonomous Long-Term Exploration on Low-Cost Robotic Hardware

Adam Seewald
Marvin Chancán
Connor M. McCann
Seonghoon Noh
Omeed Fallahi
Hector Castillo
Ian Abraham
Aaron M. Dollar

This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor and outdoor terrains. The exploration consists of a methodology that extends frontier- and sampling-based exploration with a path-following vector field and a state-of-the-art SLAM algorithm. The methodology allows the robot to explore its surroundings at lower update frequencies, enabling the use of lower-performing and lower-cost hardware while still retaining good autonomous performance. The approach further consists of a methodology to interact with a remotely located human operator based on an inexpensive long-range and low-power communication technology from the internet-of-things domain (i. e. , LoRa) and a customized communication protocol. The results and the feasibility analysis show the possible applications and limitations of the approach. Code—The open-source software stack is made available on the project repository webpage †.

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IROS Conference 2023 Conference Paper

Bi-Level Image-Guided Ergodic Exploration with Applications to Planetary Rovers

Elena Wittemyer
Ian Abraham

We present a method for image-guided exploration for mobile robotic systems. Our approach extends ergodic exploration methods, a recent exploration approach that prioritizes complete coverage of a space, with the use of a learned image classifier that automatically detects objects and updates an information map to guide further exploration and localization of objects. Additionally, to improve outcomes of the information collected by our robot's visual sensor, we present a decomposition of the ergodic optimization problem as bi-level coarse and fine solvers, which act respectively on the robot's body and the robot's visual sensor. Our approach is applied to geological survey and localization of rock formations for Mars rovers, with real images from Mars rovers used to train the image classifier. Results demonstrate 1) improved localization of rock formations compared to naive approaches while 2) minimizing the path length of the exploration through the bi-level exploration.

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IROS Conference 2023 Conference Paper

Multi-Agent Multi-Objective Ergodic Search Using Branch and Bound

Akshaya Kesarimangalam Srinivasan
Geordan Gutow
Zhongqiang Ren
Ian Abraham
Bhaskar Vundurthy
Howie Choset

Search and rescue applications often need multiple agents to complete a set of conflicting tasks. This paper studies a Multi-Agent Multi-Objective Ergodic Search (MA-MO-ES) approach to this problem where each objective or task is to cover a domain subject to an information map. The goal is to allocate coverage tasks to agents so that all maps are explored ergodically. The combinatorial nature of task allocation makes it computationally expensive to solve for optimal allocation using brute force. Apart from a large number of possible allocations, computing the cost of a task allocation is itself an expensive planning problem. To mitigate the computational challenge, we present a branch and bound-based algorithm with pruning techniques that reduce the number of allocations to be searched to find optimal coverage task allocation. We also present an approach to leverage the similarity between information maps to further reduce computation. Extensive testing on 147 randomly generated test cases shows an order of magnitude improvement in runtime compared to an exhaustive brute force approach.

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ICRA Conference 2023 Conference Paper

Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions

Cameron J. Lerch
Dayi Dong
Ian Abraham

In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe (collision-free) trajectories is a challenging problem that has garnered significant due to its importance in the successful utilization of robots in search and exploration tasks. This work contributes a method that generates guaranteed safety-critical search trajectories in a cluttered environment. Our approach integrates safety-critical constraints using discrete control barrier functions (DCBFs) with ergodic trajectory optimization to enable safe exploration. Ergodic trajectory optimization plans continuous exploratory trajectories that guarantee complete coverage of a space. We demonstrate through simulated and experimental results on a drone that our approach is able to generate trajectories that enable safe and effective exploration. Furthermore, we show the efficacy of our approach for safe exploration using real-world single- and multi- drone platforms.

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ICRA Conference 2022 Conference Paper

Multi-Agent Dynamic Ergodic Search with Low-Information Sensors

Howard Coffin
Ian Abraham
Guillaume Sartoretti
Tyler Dillstrom
Howie Choset

The long-term goal of this work is to enable agents with low-information sensors to perform tasks usually restricted to ones with more sophisticated, high-information sensing capabilities. Our approach is to regulate the motion of these low-information agents to obtain “high-information” results. As a first step, we consider a multi-agent system tasked with locating and tracking a moving target using only noisy binary sensors that measure the presence (or lack thereof) of a target in the sensor's field of view. To generate effective paths for these agents, we use ergodic trajectory optimization with a novel mutual information map that is fast to compute and can handle the discontinuous measurement models often associated with low-information sensing. We compare our approach with existing motion planning methods in multiple simulated experiments. Our experiments show that agents using our method outperform purely coverage-based approaches as well as naive ergodic approaches.

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IROS Conference 2021 Conference Paper

Linear Policies are Sufficient to Enable Low-Cost Quadrupedal Robots to Traverse Rough Terrain

Maurice Rahme
Ian Abraham
Matthew L. Elwin
Todd D. Murphey

The availability of inexpensive 3D-printed quadrupedal robots motivates the development of learning-based methods compatible with low-cost embedded processors and position-controlled hobby servos. In this work, we show that a linear policy is sufficient to modulate an open-loop trajectory generator, enabling a quadruped to walk over rough, unknown terrain, with limited sensing. The policy is trained in simulation using randomized terrain and dynamics and directly deployed on the robot. We show that the resulting controller can be implemented on resource-constrained systems. We demonstrate the results by deploying the policy on the OpenQuadruped, an open-source 3D-printed robot equipped with hobby servos and an embedded microprocessor.

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