Humans are hard to model and predict; thus, it is hard for a robot to consider how other people in the environment may act in its decision-making process. Finding efficient and effective ways to model human behavior to maintain the safety of people around a robot or to allow a robot to fluently collaborate with other people (or perhaps even other agents) is critical to deploying robots outside controlled environments such as the lab or factory.
Moreover, as robots become more capable agents and enter the real world, systems today appear in the workplace, home, and public spaces. Ethical considerations of how robots should operate are trailing behind algorithmic advances, and these advances do not necessarily consider the other people the robot is working around explicitly. Suppose the algorithms that control a robot’s behavior are agnostic to human concerns. In that case, they will be (un)intentionally exploited by malicious, unethical, or ignorant actors to violate privacy, injure and disturb people, and further increase inequity.
2024
arXiv
CaStL: Constraints as Specifications through LLM Translation for Long-Horizon Task and Motion Planning
Large Language Models (LLMs) have demonstrated remarkable ability in long-horizon Task and Motion Planning (TAMP) by translating clear and straightforward natural language problems into formal specifications such as the Planning Domain Definition Language (PDDL). However, real-world problems are often ambiguous and involve many complex constraints. In this paper, we introduce Constraints as Specifications through LLMs (CaStL), a framework that identifies constraints such as goal conditions, action ordering, and action blocking from natural language in multiple stages. CaStL translates these constraints into PDDL and Python scripts, which are solved using an custom PDDL solver. Tested across three PDDL domains, CaStL significantly improves constraint handling and planning success rates from natural language specification in complex scenarios.
@misc{guo2024castl,title={{CaStL}: Constraints as Specifications through {LLM} Translation for Long-Horizon Task and Motion Planning},author={Guo, Weihang and Kingston, Zachary and Kavraki, Lydia E.},year={2024},eprint={2410.22225},archiveprefix={arXiv},primaryclass={cs.RO},note={Under Review},}
Abstract
Perception-aware Planning for Robotics: Challenges and Opportunities
In this work, we argue that new methods are needed to generate robot motion for navigation or manipulation while effectively achieving perception goals. We support our argument by conducting experiments with a simulated robot that must accomplish a primary task, such as manipulation or navigation, while concurrently monitoring an object in the environment. Our preliminary study demonstrates that a decoupled approach fails to achieve high success in either action-focused motion generation or perception goals, motivating further developments of approaches that holistically consider both goals.
@inproceedings{meng2024icra40,author={Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Unhelkar, Vaibhav and Kavraki, Lydia E.},title={Perception-aware Planning for Robotics: Challenges and Opportunities},booktitle={40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)},year={2024},}
Workshop
Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective
@inproceedings{Meng2024wksp,author={Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Fontenot, Nicole M. and Hamlin, Shannan K. and Unhelkar, Vaibhav and Kavraki, Lydia E.},title={Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective},booktitle={IEEE ICRA 2024 Workshop---Workshop on Nursing Robotics},year={2024},}
Robotics and automation are poised to change the landscape of home and work in the near future. Robots are adept at deliberately moving, sensing, and interacting with their environments. The pervasive use of this technology promises societal and economic payoffs due to its capabilities - conversely, the capabilities of robots to move within and sense the world around them is susceptible to abuse. Robots, unlike typical sensors, are inherently autonomous, active, and deliberate. Such automated agents can become AI double agents liable to violate the privacy of coworkers, privileged spaces, and other stakeholders. In this work we highlight the understudied and inevitable threats to privacy that can be posed by the autonomous, deliberate motions and sensing of robots. We frame the problem within broader sociotechnological questions alongside a comprehensive review. The privacy-aware motion planning problem is formulated in terms of cost functions that can be modified to induce privacy-aware behavior - preserving, agnostic, or violating. Simulated case studies in manipulation and navigation, with altered cost functions, are used to demonstrate how privacy-violating threats can be easily injected, sometimes with only small changes in performance (solution path lengths). Such functionality is already widely available. This preliminary work is meant to lay the foundations for near-future, holistic, interdisciplinary investigations that can address questions surrounding privacy in intelligent robotic behaviors determined by planning algorithms.
@inproceedings{shome2023privacy,author={Shome, Rahul and Kingston, Zachary and Kavraki, Lydia E.},title={Robots as AI Double Agents: Privacy in Motion Planning},booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},year={2023},volume={},number={},pages={2861-2868},doi={10.1109/IROS55552.2023.10341460},}
Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of human moves. The limit on human moves is unintuitive, and in many settings determinism is undesirable. In this paper, we present a novel planning method for collaborative human-robot manipulation tasks via probabilistic synthesis. We introduce a probabilistic manipulation domain that captures the interaction by allowing for both robot and human actions with states that represent the configurations of the objects in the workspace. The task is specified using Linear Temporal Logic over finite traces (LTLf). We then transform our manipulation domain into a Markov Decision Process (MDP) and synthesize an optimal policy to satisfy the specification on this MDP. We present two novel contributions: a formalization of probabilistic manipulation domains allowing us to apply existing techniques and a comparison of different encodings of these domains. Our framework is validated on a physical UR5 robot.
@inproceedings{wells2021icra,author={Wells, Andrew M. and Kingston, Zachary and Lahijanian, Morteza and Kavraki, Lydia E. and Vardi, Moshe Y.},title={Finite Horizon Synthesis for Probabilistic Manipulation Domains},booktitle={IEEE International Conference on Robotics and Automation},year={2021},doi={10.1109/ICRA48506.2021.9561297},pages={6336--6342},}