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Aidan Curtis

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11 papers

2 author rows

ICML Conference 2025 Conference Paper

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills

Aidan Curtis
Eric Li
Michael Noseworthy
Nishad Gothoskar
Sachin Chitta
Hui Li
Leslie Pack Kaelbling
Nicole E. Carey

Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies learned in simulation. By randomizing properties of the environment during training, the learned policy can be robust to uncertainty along the randomized dimensions. While the environment distribution is typically specified by hand, in this paper we investigate the problem of automatically discovering this sampling distribution via entropy-regularized reward maximization of a neural sampling distribution in the form of a normalizing flow. We show that this architecture is more flexible and results in better robustness than existing approaches to learning simple parameterized sampling distributions. We demonstrate that these policies can be used to learn robust policies for contact-rich assembly tasks. Additionally, we explore how these sampling distributions, in combination with a privileged value function, can be used for out-of-distribution detection in the context of an uncertainty-aware multi-step manipulation planner.

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ICRA Conference 2023 Conference Paper

Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects

Aidan Curtis
Leslie Pack Kaelbling
Siddarth Jain

Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.

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ICRA Conference 2023 Conference Paper

Visibility-Aware Navigation Among Movable Obstacles

Jose Muguira-Iturralde
Aidan Curtis
Yilun Du
Leslie Pack Kaelbling
Tomás Lozano-Pérez

In this paper, we examine the problem of visibility-aware robot navigation among movable obstacles (VANAMO). A variant of the well-known NAMO robotic planning problem, VANAMO puts additional visibility constraints on robot motion and object movability. This new problem formulation lifts the restrictive assumption that the map is fully visible and the object positions are fully known. We provide a formal definition of the VANAMO problem and propose the Look and Manipulate Backchaining (LAMB) algorithm for solving such problems. Lamb has a simple vision-based interface that makes it more easily transferable to real-world robot applications and scales to the large 3D environments. To evaluate Lamb, we construct a set of tasks that illustrate the complex interplay between visibility and object movability that can arise in mobile base manipulation problems in unknown environments. We show that Lamb outperforms NAMO and visibility-aware motion planning approaches as well as simple combinations of them on complex manipulation problems with partial observability.

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AAAI Conference 2022 Conference Paper

Discovering State and Action Abstractions for Generalized Task and Motion Planning

Aidan Curtis
Tom Silver
Joshua B. Tenenbaum
Tomás Lozano-Pérez
Leslie Kaelbling

Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few training examples and applied to an entire domain of problems. Generalized planning approaches perform well in discrete AI planning problems that involve large numbers of objects and extended action sequences to achieve the goal. In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. Additionally, we show that these simple generalized plans learned from only a handful of examples can be used to improve the search efficiency of TAMP solvers.

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ICRA Conference 2022 Conference Paper

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances

Aidan Curtis
Xiaolin Fang 0002
Leslie Pack Kaelbling
Tomás Lozano-Pérez
Caelan Reed Garrett

We present a strategy for designing and building very general robot manipulation systems using a general-purpose task-and-motion planner with both engineered and learned modules that estimate properties and affordances of unknown objects. Such systems are closed-loop policies that map from RGB images, depth images, and robot joint encoder measurements to robot joint position commands. We show that this strategy leads to intelligent behaviors even without a priori knowledge regarding the set of objects, their geometries, and their affordances. We show how these modules can be flexibly composed with robot-centric primitives using the PDDLStream task and motion planning framework. Finally, we demonstrate that this strategy can enable a single policy to perform a wide variety of real-world multi-step manipulation tasks, generalizing over a broad class of objects, arrangements, and goals, without prior knowledge of the environment or re-training.

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ICLR Conference 2022 Conference Paper

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments

Sugandha Sharma
Aidan Curtis
Marta Kryven
Joshua B. Tenenbaum
Ila Rani Fiete

Humans are expert explorers and foragers. Understanding the computational cognitive mechanisms that support this capability can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments by inferring the structure of unobserved spaces through re-use of spatial information collected from previously explored spaces. Taking inspiration from the neuroscience of repeating map fragments and ideas about program induction, we present a novel ``Map Induction'' framework, which involves the generation of novel map proposals for unseen environments based on compositions of already-seen spaces in a Hierarchical Bayesian framework. The model thus explicitly reasons about unseen spaces through a distribution of strong spatial priors. We introduce a new behavioral Map Induction Task (MIT) that involves foraging for rewards to compare human performance with state-of-the-art existing models and Map Induction. We show that Map Induction better predicts human behavior than the non-inductive baselines. We also show that Map Induction, when used to augment state-of-the-art approximate planning algorithms, improves their performance.

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IJCAI Conference 2022 Conference Paper

PG3: Policy-Guided Planning for Generalized Policy Generation

Ryan Yang
Tom Silver
Aidan Curtis
Tomas Lozano-Perez
Leslie Kaelbling

A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies. We demonstrate limitations of two score functions --- policy evaluation and plan comparison --- and propose a new approach that overcomes these limitations. The main idea behind our approach, Policy-Guided Planning for Generalized Policy Generalization (PG3), is that a candidate policy should be used to guide planning on training problems as a mechanism for evaluating that candidate. Theoretical results in a simplified setting give conditions under which PG3 is optimal or admissible. We then study a specific instantiation of policy search where planning problems are PDDL-based and policies are lifted decision lists. Empirical results in six domains confirm that PG3 learns generalized policies more efficiently and effectively than several baselines.

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PRL Workshop 2022 Workshop Paper

PG3: Policy-Guided Planning for Generalized Policy Generation

Ryan Yang
Tom Silver
Aidan Curtis
Tomas Lozano-Perez
Leslie Kaelbling

A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies. We demonstrate limitations of two score functions — policy evaluation and plan comparison — and propose a new approach that overcomes these limitations. The main idea behind our approach, Policy-Guided Planning for Generalized Policy Generalization (PG3), is that a candidate policy should be used to guide planning on training problems as a mechanism for evaluating that candidate. Theoretical results in a simplified setting give conditions under which PG3 is optimal or admissible. We then study a specific instantiation of policy search where planning problems are PDDL-based and policies are lifted decision lists. Empirical results in six domains confirm that PG3 learns generalized policies more efficiently and effectively than several baselines.

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AAAI Conference 2021 Conference Paper

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

Tom Silver
Rohan Chitnis
Aidan Curtis
Joshua B. Tenenbaum
Tomás Lozano-Pérez
Leslie Pack Kaelbling

Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https: //youtu. be/FWsVJc2fvCE Code: https: //git. io/JIsqX

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NeurIPS Conference 2021 Conference Paper

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

Chuang Gan
Jeremy Schwartz
Seth Alter
Damian Mrowca
Martin Schrimpf
James Traer
Julian De Freitas
Jonas Kubilius

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable ``avatars” that embody AI agents; and support for human interactions with VR devices. TDW’s API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that ‘learn like a child’, and attention studies in humans and neural networks.

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ICML Conference 2020 Conference Paper

Flexible and Efficient Long-Range Planning Through Curious Exploration

Aidan Curtis
Minjian Xin
Dilip Arumugam
Kevin T. Feigelis
Daniel L. K. Yamins

Identifying algorithms that flexibly and efficiently discover temporally-extended multi-phase plans is an essential step for the advancement of robotics and model-based reinforcement learning. The core problem of long-range planning is finding an efficient way to search through the tree of possible action sequences. Existing non-learned planning solutions from the Task and Motion Planning (TAMP) literature rely on the existence of logical descriptions for the effects and preconditions for actions. This constraint allows TAMP methods to efficiently reduce the tree search problem but limits their ability to generalize to unseen and complex physical environments. In contrast, deep reinforcement learning (DRL) methods use flexible neural-network-based function approximators to discover policies that generalize naturally to unseen circumstances. However, DRL methods struggle to handle the very sparse reward landscapes inherent to long-range multi-step planning situations. Here, we propose the Curious Sample Planner (CSP), which fuses elements of TAMP and DRL by combining a curiosity-guided sampling strategy with imitation learning to accelerate planning. We show that CSP can efficiently discover interesting and complex temporally-extended plans for solving a wide range of physically realistic 3D tasks. In contrast, standard planning and learning methods often fail to solve these tasks at all or do so only with a huge and highly variable number of training samples. We explore the use of a variety of curiosity metrics with CSP and analyze the types of solutions that CSP discovers. Finally, we show that CSP supports task transfer so that the exploration policies learned during experience with one task can help improve efficiency on related tasks.

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