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Naman Shah

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions

Rashmeet Kaur Nayyar
Naman Shah
Siddharth Srivastava

Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisions about continuous action parameters governing how an action is executed. Existing approaches exhibit severe limitations in this setting---planning methods demand hand-crafted action models, and standard reinforcement learning (RL) algorithms are designed for either discrete or continuous actions but not both, and the few RL methods that handle parameterized actions typically rely on domain-specific engineering and fail to exploit the latent structure of these spaces. This paper extends the scope of RL algorithms to long-horizon, sparse-reward settings with parameterized actions by enabling agents to autonomously learn both state and action abstractions online. We introduce algorithms that progressively refine these abstractions during learning, increasing fine-grained detail in the critical regions of the state–action space where greater resolution improves performance. Across several continuous-state, parameterized-action domains, our abstraction-driven approach enables TD(λ) to achieve markedly higher sample efficiency than state-of-the-art baselines.

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AAAI Conference 2024 Conference Paper

Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention

Naman Shah
Siddharth Srivastava

This paper addresses the problem of inventing and using hierarchical representations for stochastic robot-planning problems. Rather than using hand-coded state or action representations as input, it presents new methods for learning how to create a high-level action representation for long-horizon, sparse reward robot planning problems in stochastic settings with unknown dynamics. After training, this system yields a robot-specific but environment independent planning system. Given new problem instances in unseen stochastic environments, it first creates zero-shot options (without any experience on the new environment) with dense pseudo-rewards and then uses them to solve the input problem in a hierarchical planning and refinement process. Theoretical results identify sufficient conditions for completeness of the presented approach. Extensive empirical analysis shows that even in settings that go beyond these sufficient conditions, this approach convincingly outperforms baselines by 2x in terms of solution time with orders of magnitude improvement in solution quality.

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AAAI Conference 2024 Short Paper

Learning Neuro-Symbolic Abstractions for Robot Planning and Learning

Naman Shah

Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.

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PRL Workshop 2023 Workshop Paper

Learning to Create Abstraction Hierarchies for Motion Planning under Uncertainty

Naman Shah
Siddharth Srivastava

State and action hierarchies have been found to be invaluable in long-horizon robot motion planning. However, approaches for learning such hierarchies tend to require extensive experience on the target task, target environment and/or deterministic dynamics. This paper considers the problem of learning how to create state and action abstractions for a known robot with stochastic low-level controllers in previously unseen environments. We present a novel and robust approach for learning to create an abstract, searchable state space, high-level options, as well as low-level option policies in this setting. We show that this approach facilitates efficient hierarchical planning in stochastic settings with strong guarantees of composability and completeness for holonomic robots. Extensive empirical analysis with holonomic as well as non-holonomic robots on a total of $60$ different combinations unseen environments and tasks shows that the resulting approach is broadly applicable, scales well and enables effective learning and transfer even in tasks with long horizons where baselines are unable to learn.

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IJCAI Conference 2023 Conference Paper

Reliable Neuro-Symbolic Abstractions for Planning and Learning

Naman Shah

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AAMAS Conference 2022 Conference Paper

JEDAI: A System for Skill-Aligned Explainable Robot Planning

Naman Shah
Pulkit Verma
Trevor Angle
Siddharth Srivastava

This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated task and motion planning and explainable AI. JEDAI helps users create high-level, intuitive plans while ensuring that they will be executable by the robot. It also provides users customized explanations about errors and helps improve their understanding of AI planning as well as the limits and capabilities of the underlying robot system.

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AAMAS Conference 2022 Conference Paper

Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning

Naman Shah
Siddharth Srivastava

This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability. Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. We present a new approach for bootstrapping the entire hierarchical planning process. This allows us to compute abstract states and actions for new environments automatically using the critical regions predicted by a deep neural network with an auto-generated robot-specific architecture. We show that the learned abstractions can be used with a novel multi-source bi-directional hierarchical robot planning algorithm that is sound and probabilistically complete. An extensive empirical evaluation on twenty different settings using holonomic and non-holonomic robots shows that (a) our learned abstractions provide the information necessary for efficient multi-source hierarchical planning; and that (b) this approach of learning, abstractions, and planning outperforms state-of-the-art baselines by nearly a factor of ten in terms of planning time on test environments not seen during training.

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