arXiv subjects

Ahmed H. Qureshi

Publications and source records attributed to Ahmed H. Qureshi.

At least 37 records · Page 2Linked to original sources

arXiv2025

Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning

Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and unsafe. However, it remains challenging in practice due to the need to learn from datasets with limited coverage of the state-action space and to generalize across long-horizon tasks. To improve on these challenges, we propose a \emph{Physics-informed (Pi)} regularized loss for value learning, derived from the Eikonal Partial Differential Equation (PDE) and which induces a geometric inductive bias in the learned value function. Unlike generic gradient penalties that are primarily used to stabilize training, our formulation is grounded in continuous-time optimal control and encourages value functions to align with cost-to-go structures. The proposed regularizer is broadly compatible with temporal-difference-based value learning and can be integrated into existing Offline GCRL algorithms. When combined with Hierarchical Implicit Q-Learning (HIQL), the resulting method, Eikonal-regularized HIQL (Eik-HIQL), yields significant improvements in both performance and generalization, with pronounced gains in stitching regimes and large-scale navigation tasks.

cs.LG↗

arXiv2025

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments

Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with resolution control and scalability, while imitation learning-based methods require a large amount of demonstration data. Active Neural Time Fields (ANTFields) have recently emerged as a promising solution by using local observations to learn cost-to-go functions without relying on demonstrations. Despite their potential, these methods are hampered by challenges such as spectral bias and catastrophic forgetting, which diminish their effectiveness in complex scenarios. To address these issues, our approach decomposes the planning problem into a hierarchical structure. At the high level, a sparse graph captures the environment's global connectivity, while at the low level, a planner based on neural fields navigates local obstacles by solving the Eikonal PDE. This physics-informed strategy overcomes common pitfalls like spectral bias and neural field fitting difficulties, resulting in a smooth and precise representation of the cost landscape. We validate our framework in large-scale environments, demonstrating its enhanced adaptability and precision compared to previous methods, and highlighting its potential for online exploration, mapping, and real-world navigation.

cs.RO↗

arXiv2025

Physics-informed Neural Time Fields for Prehensile Object Manipulation

Object manipulation skills are necessary for robots operating in various daily-life scenarios, ranging from warehouses to hospitals. They allow the robots to manipulate the given object to their desired arrangement in the cluttered environment. The existing approaches to solving object manipulations are either inefficient sampling based techniques, require expert demonstrations, or learn by trial and error, making them less ideal for practical scenarios. In this paper, we propose a novel, multimodal physics-informed neural network (PINN) for solving object manipulation tasks. Our approach efficiently learns to solve the Eikonal equation without expert data and finds object manipulation trajectories fast in complex, cluttered environments. Our method is multimodal as it also reactively replans the robot's grasps during manipulation to achieve the desired object poses. We demonstrate our approach in both simulation and real-world scenarios and compare it against state-of-the-art baseline methods. The results indicate that our approach is effective across various objects, has efficient training compared to previous learning-based methods, and demonstrates high performance in planning time, trajectory length, and success rates. Our demonstration videos can be found at https://youtu.be/FaQLkTV9knI.

cs.RO↗

arXiv2025

Multimodal Human-Intent Modeling for Contextual Robot-to-Human Handovers of Arbitrary Objects

Human-robot object handover is a crucial element for assistive robots that aim to help people in their daily lives, including elderly care, hospitals, and factory floors. The existing approaches to solving these tasks rely on pre-selected target objects and do not contextualize human implicit and explicit preferences for handover, limiting natural and smooth interaction between humans and robots. These preferences can be related to the target object selection from the cluttered environment and to the way the robot should grasp the selected object to facilitate desirable human grasping during handovers. Therefore, this paper presents a unified approach that selects target distant objects using human verbal and non-verbal commands and performs the handover operation by contextualizing human implicit and explicit preferences to generate robot grasps and compliant handover motion sequences. We evaluate our integrated framework and its components through real-world experiments and user studies with arbitrary daily-life objects. The results of these evaluations demonstrate the effectiveness of our proposed pipeline in handling object handover tasks by understanding human preferences. Our demonstration videos can be found at https://youtu.be/6z27B2INl-s.

cs.RO↗

arXiv2025

Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks

Enabling robots to grasp and reposition human limbs can significantly enhance their ability to provide assistive care to individuals with severe mobility impairments, particularly in tasks such as robot-assisted bed bathing and dressing. However, existing assistive robotics solutions often assume that the human remains static or quasi-static, limiting their effectiveness. To address this issue, we present Manip4Care, a modular simulation pipeline that enables robotic manipulators to grasp and reposition human limbs effectively. Our approach features a physics simulator equipped with built-in techniques for grasping and repositioning while considering biomechanical and collision avoidance constraints. Our grasping method employs antipodal sampling with force closure to grasp limbs, and our repositioning system utilizes the Model Predictive Path Integral (MPPI) and vector-field-based control method to generate motion trajectories under collision avoidance and biomechanical constraints. We evaluate this approach across various limb manipulation tasks in both supine and sitting positions and compare outcomes for different age groups with differing shoulder joint limits. Additionally, we demonstrate our approach for limb manipulation using a real-world mannequin and further showcase its effectiveness in bed bathing tasks.

cs.RO↗

arXiv2025

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments

Physics-informed Neural Motion Planners (PiNMPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go function for motion planning. However, their scalability remains limited by spectral bias and the complex loss landscape of PDE-driven training. Domain decomposition mitigates these issues by dividing the environment into smaller subdomains, but existing methods enforce continuity only at individual spatial points. While effective for function approximation, these methods fail to capture the spatial connectivity required for motion planning, where the cost-to-go function depends on both the start and goal coordinates rather than a single query point. We propose Finite Basis Neural Time Fields (FB-NTFields), a novel neural field representation for scalable cost-to-go estimation. Instead of enforcing continuity in output space, FB-NTFields construct a latent space representation, computing the cost-to-go as a distance between the latent embeddings of start and goal coordinates. This enables global spatial coherence while integrating domain decomposition, ensuring efficient large-scale motion planning. We validate FB-NTFields in complex synthetic and real-world scenarios, demonstrating substantial improvements over existing PiNMPs. Finally, we deploy our method on a Unitree B1 quadruped robot, successfully navigating indoor environments. The supplementary videos can be found at https://youtu.be/OpRuCbLNOwM.

cs.RO↗

arXiv2025

Physics-informed Neural Mapping and Motion Planning in Unknown Environments

Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing mapping methods create maps that require computationally expensive motion planning tools to find a path solution. In this paper, we propose a new mapping feature called arrival time fields, which is a solution to the Eikonal equation. The arrival time fields can directly guide the robot in navigating the given environments. Therefore, this paper introduces a new approach called Active Neural Time Fields (Active NTFields), which is a physics-informed neural framework that actively explores the unknown environment and maps its arrival time field on the fly for robot motion planning. Our method does not require any expert data for learning and uses neural networks to directly solve the Eikonal equation for arrival time field mapping and motion planning. We benchmark our approach against state-of-the-art mapping and motion planning methods and demonstrate its superior performance in both simulated and real-world environments with a differential drive robot and a 6 degrees-of-freedom (DOF) robot manipulator. The supplementary videos can be found at https://youtu.be/qTPL5a6pRKk, and the implementation code repository is available at https://github.com/Rtlyc/antfields-demo.

cs.RO↗

arXiv2025

Differentiable Composite Neural Signed Distance Fields for Robot Navigation in Dynamic Indoor Environments

Neural Signed Distance Fields (SDFs) provide a differentiable environment representation to readily obtain collision checks and well-defined gradients for robot navigation tasks. However, updating neural SDFs as the scene evolves entails re-training, which is tedious, time consuming, and inefficient, making it unsuitable for robot navigation with limited field-of-view in dynamic environments. Towards this objective, we propose a compositional framework of neural SDFs to solve robot navigation in indoor environments using only an onboard RGB-D sensor. Our framework embodies a dual mode procedure for trajectory optimization, with different modes using complementary methods of modeling collision costs and collision avoidance gradients. The primary stage queries the robot body's SDF, swept along the route to goal, at the obstacle point cloud, enabling swift local optimization of trajectories. The secondary stage infers the visible scene's SDF by aligning and composing the SDF representations of its constituents, providing better informed costs and gradients for trajectory optimization. The dual mode procedure combines the best of both stages, achieving a success rate of 98%, 14.4% higher than baseline with comparable amortized plan time on iGibson 2.0. We also demonstrate its effectiveness in adapting to real-world indoor scenarios.

cs.RO↗

arXiv2025

Implicit Physics-aware Policy for Dynamic Manipulation of Rigid Objects via Soft Body Tools

Recent advancements in robot tool use have unlocked their usage for novel tasks, yet the predominant focus is on rigid-body tools, while the investigation of soft-body tools and their dynamic interaction with rigid bodies remains unexplored. This paper takes a pioneering step towards dynamic one-shot soft tool use for manipulating rigid objects, a challenging problem posed by complex interactions and unobservable physical properties. To address these problems, we propose the Implicit Physics-aware (IPA) policy, designed to facilitate effective soft tool use across various environmental configurations. The IPA policy conducts system identification to implicitly identify physics information and predict goal-conditioned, one-shot actions accordingly. We validate our approach through a challenging task, i.e., transporting rigid objects using soft tools such as ropes to distant target positions in a single attempt under unknown environment physics parameters. Our experimental results indicate the effectiveness of our method in efficiently identifying physical properties, accurately predicting actions, and smoothly generalizing to real-world environments. The related video is available at: https://youtu.be/4hPrUDTc4Rg?si=WUZrT2vjLMt8qRWA

cs.RO↗

arXiv2025

DeRi-IGP: Learning to Manipulate Rigid Objects Using Deformable Objects via Iterative Grasp-Pull

Robotic manipulation of rigid objects via deformable linear objects (DLO) such as ropes is an emerging field of research with applications in various rigid object transportation tasks. A few methods that exist in this field suffer from limited robot action and operational space, poor generalization ability, and expensive model-based development. To address these challenges, we propose a universally applicable moving primitive called Iterative Grasp-Pull (IGP). We also introduce a novel vision-based neural policy that learns to parameterize the IGP primitive to manipulate DLO and transport their attached rigid objects to the desired goal locations. Additionally, our decentralized algorithm design allows collaboration among multiple agents to manipulate rigid objects using DLO. We evaluated the effectiveness of our approach in both simulated and real-world environments for a variety of soft-rigid body manipulation tasks. In the real world, we also demonstrate the effectiveness of our decentralized approach through human-robot collaborative transportation of rigid objects to given goal locations. We also showcase the large operational space of IGP primitive by solving distant object acquisition tasks. Lastly, we compared our approach with several model-based and learning-based baseline methods. The results indicate that our method surpasses other approaches by a significant margin.

cs.RO↗

arXiv2024

Integrating Active Sensing and Rearrangement Planning for Efficient Object Retrieval from Unknown, Confined, Cluttered Environments

Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper presents a new, integrated heuristic-based active sensing and Monte-Carlo Tree Search (MCTS)-based retrieval planning approach. These components provide feedback to one another to actively sense critical, unobserved areas suitable for the retrieval planner to plan a sequence for relocating path-blocking obstacles and a collision-free trajectory for retrieving the target object. We demonstrate the effectiveness of our approach using a robot arm equipped with an in-hand camera in both simulated and real-world confined, cluttered scenarios. Our framework is compared against various state-of-the-art methods. The results indicate that our proposed approach outperforms baseline methods by a significant margin in terms of the success rate, the object rearrangement planning time consumption and the number of planning trials before successfully retrieving the target. Videos can be found at https://youtu.be/tea7I-3RtV0.

cs.RO↗

arXiv2024

Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments

Non-monotone object rearrangement planning in confined spaces such as cabinets and shelves is a widely occurring but challenging problem in robotics. Both the robot motion and the available regions for object relocation are highly constrained because of the limited space. This work proposes a Multi-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-monotone object rearrangement planning problems in confined spaces. Our approach decouples the complex problem into simpler subproblems using an object stage topology. A subgoal-focused tree expansion algorithm that jointly considers the high-level planning and the low-level robot motion is designed to reduce the search space and better guide the search process. By fitting the task into the MCTS paradigm, our method produces optimistic solutions by balancing exploration and exploitation. The experiments demonstrate that our method outperforms the existing methods in terms of the planning time, the number of steps, and the total move distance. Moreover, we deploy our MS-MCTS to a real-world robot system and verify its performance in different scenarios.

cs.RO↗

arXiv2024

Physics-informed Neural Motion Planning on Constraint Manifolds

Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in various scenarios ranging from object manipulation to legged-robot locomotion. However, the zero-volume nature of manifolds makes the CMP problem challenging, and the state-of-the-art methods still take several seconds to find a path and require a computationally expansive path dataset for imitation learning. Recently, physics-informed motion planning methods have emerged that directly solve the Eikonal equation through neural networks for motion planning and do not require expert demonstrations for learning. Inspired by these approaches, we propose the first physics-informed CMP framework that solves the Eikonal equation on the constraint manifolds and trains neural function for CMP without expert data. Our results show that the proposed approach efficiently solves various CMP problems in both simulation and real-world, including object manipulation under orientation constraints and door opening with a high-dimensional 6-DOF robot manipulator. In these complex settings, our method exhibits high success rates and finds paths in sub-seconds, which is many times faster than the state-of-the-art CMP methods.

cs.RO↗

arXiv2024

Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot's In-hand RGB-D Sensor

Rearrangement planning for object retrieval tasks from confined spaces is a challenging problem, primarily due to the lack of open space for robot motion and limited perception. Several traditional methods exist to solve object retrieval tasks, but they require overhead cameras for perception and a time-consuming exhaustive search to find a solution and often make unrealistic assumptions, such as having identical, simple geometry objects in the environment. This paper presents a neural object retrieval framework that efficiently performs rearrangement planning of unknown, arbitrary objects in confined spaces to retrieve the desired object using a given robot grasp. Our method actively senses the environment with the robot's in-hand camera. It then selects and relocates the non-target objects such that they do not block the robot path homotopy to the target object, thus also aiding an underlying path planner in quickly finding robot motion sequences. Furthermore, we demonstrate our framework in challenging scenarios, including real-world cabinet-like environments with arbitrary household objects. The results show that our framework achieves the best performance among all presented methods and is, on average, two orders of magnitude computationally faster than the best-performing baselines.

cs.RO↗

arXiv2024

Language-guided Active Sensing of Confined, Cluttered Environments via Object Rearrangement Planning

Language-guided active sensing is a robotics subtask where a robot with an onboard sensor interacts efficiently with the environment via object manipulation to maximize perceptual information, following given language instructions. These tasks appear in various practical robotics applications, such as household service, search and rescue, and environment monitoring. Despite many applications, the existing works do not account for language instructions and have mainly focused on surface sensing, i.e., perceiving the environment from the outside without rearranging it for dense sensing. Therefore, in this paper, we introduce the first language-guided active sensing approach that allows users to observe specific parts of the environment via object manipulation. Our method spatially associates the environment with language instructions, determines the best camera viewpoints for perception, and then iteratively selects and relocates the best view-blocking objects to provide the dense perception of the region of interest. We evaluate our method against different baseline algorithms in simulation and also demonstrate it in real-world confined cabinet-like settings with multiple unknown objects. Our results show that the proposed method exhibits better performance across different metrics and successfully generalizes to real-world complex scenarios.

cs.RO↗

arXiv2023

MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments

Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. This paper proposes a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of simulated and real-world environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches.

cs.RO↗

arXiv2023

SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving

Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to mitigate the increase in computational complexity associated with the number of agents in the scene, some works leverage Euclidean distance to prune far-away agents. However, distance-based metric alone is insufficient to select relevant agents and accurately perform their predictions. To resolve these issues, we propose the Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information and optimally select relevant agents for motion prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism to extract global encodings. These encodings along with agents' local information, are passed through an encoder to obtain time-dependent latent variables for a motion policy predicting the future trajectories. Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.

cs.CV↗

arXiv2023

Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications

Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics. This paper presents a novel reinforcement learning approach to solving high-dimensional robot navigation tasks with complex logic specifications by co-learning planning and control policies. Notably, this approach significantly reduces the sample complexity in training, allowing us to train high-quality policies with much fewer samples compared to existing reinforcement learning algorithms. In addition, our methodology streamlines complex specification extraction from map images and enables the efficient generation of long-horizon robot motion paths across different map layouts. Moreover, our approach also demonstrates capabilities for high-dimensional control and avoiding suboptimal policies via policy alignment. The efficacy of our approach is demonstrated through experiments involving simulated high-dimensional quadruped robot dynamics and a real-world differential drive robot (TurtleBot3) under different types of task specifications.

cs.RO↗