Author name cluster

Anthony Favier

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5 papers

2 author rows

HAXP Workshop 2025 Workshop Paper

A Collaborative Numeric Task Planning Framework based on Constraint Translations using LLMs

Anthony Favier
Ngoc La
Pulkit Verma
Julie Shah

Automated planning systems require formal constraint specifications that create significant barriers for domain experts not familiar with those formal specifications, thereby limiting the practical adoption of powerful planning tools in collaborative planning settings. To overcome this challenge, we propose an LLM-based pipeline to translate human natural language constraints into formal hard-trajectory constraints. The initial user input is first refined and decomposed into more explicit natural language constraints, both preparing constraints for formal encoding and offering a chance for the human to review and correct any misinterpretation. Then, the decomposed constraints are encoded into PDDL3. By integrating this with an automated planner, a graphical interface, and PDSim, we created a closed loop where the human gets plan simulations as feedback to their natural language constraints. This innovative collaborative planning framework enables users to leverage their intuition and expertise to intuitively guide automated planning without time-consuming programming expert interventions. Through an ablation study, we demonstrate how our approach significantly improves the syntax and semantic accuracy of the translations compared to direct LLM translations. Our results demonstrate the potential of collaborative planning without technical expert interventions for higher-quality automated solving. On the other hand, our negative results seem to highlight the limitations of using PDDL3 constraints to leverage human high-level guidance as we expected, raising interesting reflections and potential discussions.

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HAXP Workshop 2024 Workshop Paper

Human-Aware Epistemic Task Planning for Human-Robot Collaboration

Shashank Shekhar
Anthony Favier
Rachid Alami

We present a novel human-aware epistemic planning framework designed for collaborative human-robot interactions, specially tailored for situations where the agents’ shared execution experiences can be interrupted by the uncontrollable nature of humans. Our objective is to generate a robot policy that accounts for such uncontrollable behaviors, thus enabling the anticipation of potential progress achieved by the robot when the experience is not shared, e. g. , when humans are briefly absent from the shared environment to complete a subtask. But this anticipation is considered from the perspective of humans who keep an estimated robot’s model. As a first step to address it, we propose a general planning framework and build a solver based on AND/OR search which integrates knowledge reasoning; this includes assessing situations by perspective taking. Our approach dynamically models and manages the expansion or contraction of potential worlds while tracking whether or not agents share the task execution experiences. This helps the planner (or the robot) to prepare itself with a set of worlds that humans would consider possible. The robot assesses the situation from the human perspective and removes the worlds that it has reason to think are impossible. However, there might still be an impossible world that is indistinguishable from the real world. In different situations, thanks to our planning framework, the robot’s policy built offline can determine an appropriate course of action, such as answering human queries, explicitly communicating some fact without being annoying, or taking appropriate action in the presence of the human to help them narrow down the possibilities further, facilitating collaboration. Our preliminary experiments show that the framework is effective for behavior planning in different situations. We discuss the practical issues in different problem settings.

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ICRA Conference 2022 Conference Paper

HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

Guilhem Buisan
Anthony Favier
Amandine Mayima
Rachid Alami 0001

The variety and complexity of tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots collaborating with humans. Indeed, humans are either requested for punctual help or are given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach, named Human-Aware Task Planner with Emulation of Human Decisions and Actions (HATP/EHDA), is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human). We present the rationale of our approach along with a formalization and show its potential on an illustrative example.

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IROS Conference 2022 Conference Paper

Watch out! There may be a Human. Addressing Invisible Humans in Social Navigation

Phani-Teja Singamaneni
Anthony Favier
Rachid Alami 0001

Current approaches in human-aware or social robot navigation address the humans that are visible to the robot. However, it is also important to address the possible emergences of humans to avoid shocks or surprises to humans and erratic behavior of the robot planner. In this paper, we propose a novel approach to detect and address these human emergences called ‘invisible humans’. We determine the places from which a human, currently not visible to the robot, can appear suddenly and then adapt the path and speed of the robot with the anticipation of potential collisions. This is done while still considering and adapting humans present in the robot's field of view. We also show how this detection can be exploited to identify and address the doorways or narrow passages. Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments.

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IROS Conference 2021 Conference Paper

Human-Aware Navigation Planner for Diverse Human-Robot Interaction Contexts

Phani-Teja Singamaneni
Anthony Favier
Rachid Alami 0001

As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of human-robot contexts. We present the architecture of the system and discuss the features along with some implementation details. Then we present a detailed analysis of various simulated human-robot contexts using the proposed planner. Further, we show that our system performs better when compared with an exiting human-aware planner in various contexts. Finally, we show the results in a real-world scenario after deploying our system on a real robot.

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